priority assignment for tasks

9 Tips On How To Prioritize Tasks Effectively At Work

There’s nothing more satisfying than checking off work on your to-do list. But before you attack your task list, you want to learn how to prioritize tasks. 

Prioritizing tasks helps you:

• Meet deadlines by getting the most critical work done first.
• Better utilize scarce time and team resources.
• Effectively manage your team’s workload.

Project management tools help you capture, prioritize, and organize your work. You can also use simple task management tools — from simple to-do lists to visual Kanban boards — to get things done. But, irrespective of the tool, you need to learn how to prioritize work.

In this article, you’ll learn about prioritization methods. Here’s what we’ll cover:

• Make A Master Task List
• Categorize Tasks Using The 4Ds Of Time Management
• Prioritizing Tasks Using Eisenhower Power Matrix
• Prioritize Project Tasks Using The MoSCoW Method
• Prioritize Using Relative Priorities
• Focus On The Most Important Tasks Of The Day
• Do The Most Difficult Task First
• Prioritize Using The Pareto Principle
• Review & Revise Task Priorities

Ready to get started, let’s begin.

1. Make A Master Task List

Task requests come from various sources — your boss, different team members, colleagues, partners, and clients. Task requests can also come from multiple channels — emails, slack messages, or even a watercooler talk.

You need a way to capture all these requests and identify top priorities. 

In addition, a task list also helps keep track of project progress. Plus, taking up the task request then, can mess up the rest of your schedule.

This is why you need a way to capture all your work in one place. It could be a notebook, a to-do list app on your phone, or the task backlog in your project management tool .

Once you have a list of all your tasks ready, you can easily categorize and prioritize them.

2. Categorize Tasks Using The 4Ds Of Time Management

Before you can prioritize your task items, you need to categorize each task in your master list into one of these four categories:

• Do the task now
• Defer the task to a later time
• Delegate the task to someone else
• Delete the task from your list

The “Do, Defer, Delegate, Delete” framework is also called the 4Ds of time management . 

You can do a few tasks on your task list quickly. They take less than a minute or two. And there’s no dependency on anyone else. Go ahead and do these to trim down your to-do list.

A few other tasks can be done by your subordinates or team members. They have the skills and the necessary information to complete the work. Delegate this work to trim down your task list further.

You’ll also find some tasks that you don’t need to do anymore. They have been on your task list forever. Or, compared to the estimated effort involved, they provide very little value. Delete these from your list.

The only work that you’re left with now is the deferred tasks. You cannot do, delegate, or delete these. This is the work that needs prioritizing. 

Next, let’s look at three ways to organize your tasks by priority.

3. Prioritizing Tasks Using Eisenhower Power Matrix

The Eisenhower four-quadrant, power matrix is a straightforward framework for prioritizing work. 

Dwight Eisenhower — the 34th president of the United States — conceptualized the power matrix. But it was Steven Covey who made it popular in his best-seller — The 7 Habits Of Highly Effective People”.

The power matrix framework helps you answer two questions. Which tasks are important? And which tasks can be eliminated altogether?

To prioritize work using this framework, put each task into one of the four quadrants:

• Urgent and important
• Important, but not urgent
• Urgent, but not important
• Neither urgent nor important

Urgent and important tasks have the highest priority. Not doing these asap will have negative consequences. 

Important but not urgent tasks will take up most of your time. Avoid neglecting these until the last minute to prevent unbalanced schedules and workloads.

Urgent but not important tasks can be delegated to your team. You don’t have to do them yourself.

Finally, you can eliminate tasks that are neither urgent nor important.

4. Prioritize Project Tasks Using The MoSCoW Method

Budget and time constraints are big challenges in project management. These challenges often directly affect the project’s outcomes.

The MoSCoW method helps prioritize work based on outcomes. Thus, it’s most effective when managing projects. 

It was developed by Dai Clegg, a software engineer, during his tenure at Oracle. 

This method helps stakeholders and clients understand the significance of each outcome in the final project delivery.

To use MoSCoW prioritization, divide your project outcomes into one of the four buckets:

• Must have: Outcomes without which the entire project is considered a failure
• Should have: Outcomes that are not as critical as must-have outcomes but still important
• Could have: Outcomes that can be delivered when you have budget or time remaining for final project delivery
• Won’t have: Outcomes that won’t be delivered with the project

Once you’ve categorized the outcomes above, you can prioritize work that supports these outcomes.

5. Order Tasks Using Relative Priorities

Using the prioritization strategies above often helps. But how do you order tasks with similar priority? What if you have lots of tasks that are important but not urgent? Or tasks that support must-have outcomes? 

How do you prioritize items that have the same priorities? What prioritization methods do you use?

The answer is simple — using relative priority. Relative priority works by weighing the importance of each task compared to other tasks on the priority list. Then ordering the tasks based on this weight. 

For example, let’s say you have ten work items. Assign a number from 1-10 to each task. And in no time, you’ll have sorted high and low priority tasks.

But, how do you decide which task is more important?

Two straightforward ways are to order work by their due dates and by dependencies. Tasks with an earlier deadline become priority tasks. Also, complete tasks that block other work from starting first.

Next, let’s look at three techniques that’ll help you breeze through your personal, prioritized task list.

6. Focus On The Most Important Tasks Of The Day

Leo Babuta of Zen Habits popularized the Most Important Tasks (MITs) method. This prioritization technique can help if you struggle to get daily tasks done even after prioritizing them.

To use the MITs method, choose up to three tasks that you want to get done today. These are your most important tasks. Complete these first. 

Then if time permits, you can take up other miscellaneous work. This extra work is a bonus. You only work on bonus tasks if you finish the MITs.

You can consider your day successful even if you just do your MITs.

7. Do The Most Unwanted Task First

But what if you struggle to get through your MITs?

Thanks to Bryan Tracy’s method and his book — Eat That Frog! — you’ll have an answer in the following few paragraphs.

But what do frogs have in common with Task prioritization?

Mark Twain has the answer:

If it’s your job to eat a frog, it’s best to do it first thing in the morning. And if it’s your job to eat two frogs, it’s best to eat the biggest one first.

Once you’ve eaten the frog, there’s nothing worse you’ll have to tackle for the rest of the day. Replace frogs with tasks, and you’ll have your answer.

Most people put off complex tasks. And not because they’re less important. But only because they seem difficult. Bryan Tracy recommends you get unwanted work out of your way first. Then getting through the rest of the work becomes a breeze.

8. Prioritize Tasks Using The Pareto Principle

If you hate frogs like most people, the Pareto Principle is another popular way to blast through your daily task list. It’s also known as the 80/20 rule or the “law of the vital few.”

According to this principle, 80% of your day’s success depends on 20% of the tasks. 

It’s tricky to identify the 20% work. The best way is to look at your MITs and pick one task you feel will make your day a success. For example, it could be an important task that provides the most value or takes up the most time. 

Once you’ve identified the task, stay focused on getting that one task done.

Together the Pareto Principle and Bryan Tracy’s methods will help you blast through your daily task list.

9. Review & Revise Task Priorities

Do you know what the most frustrating thing about managing work is?

You’re halfway through your work plan, and suddenly tasks and/or priorities change. There’s little you can do with this change. It’s a risk that you have to live with. 

Because of these changes, it’s critical to review and revise project task priorities often. 

If budget, resource, or time constraints change, revise project priorities using the 4Ds framework, Eisenhower’s Power Matrix, or the MoSCoW method.

And if short-term priorities change, review and revise your relative task priorities.

Armed with the tips in this article, you’re now ready to tackle work and bring clarity to your team.

Speaking of clarity, you may want to check out Toggl Plan . It’s a beautifully simple work management tool that provides clear visual cues about what needs to be done, by whom, and when. For project managers, Toggl Plan comes with easy, drag-and-drop timelines that make planning work a breeze.

Start your free Toggl Plan team trial now .

Jitesh is an SEO and content specialist. He manages content projects at Toggl and loves sharing actionable tips to deliver projects profitably.

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Priority Assignment to Tasks in Operating System

Assigning priority to tasks : When the number of tasks with different relative deadlines are more than the priority levels supported by the operating system, then some tasks share the same priority value. But the exact method of assigning priorities to tasks can proficiently affect the utilization of processor.

If the tasks are randomly selected for sharing the same priority level then the utilization of the processor would be lessen. It is required to select the tasks systematically to share a priority level so that the achievable schedulable utilization would be higher.

There are several priority assignment methods used when tasks share the same priority level. Some of the most used methods are:

1. Uniform Priority Assignment : In this assignment method, all the tasks are uniformly divided among the available priority levels. If the number of priority levels completely divides the number of tasks then uniform division tasks among priority levels can be done easily.

For example, if there are 20 tasks to be scheduled and 4 priority levels are supported by the operating system then each priority level is assigned 5 tasks. If uniform division is not possible i.e there are N tasks and p priority levels and N % p > 0 then floor(N/p) tasks are assigned to each level and remaining tasks are assigned to lower priority levels.

For example, if there are 10 tasks to be scheduled and 4 priority levels are supported by the operating system then firstly floor(10/4) i.e. 2 tasks are assigned to each priority levels and remaining 2 tasks are assigned to one each to lower priority levels.

2. Arithmetic Priority Assignment : In this assignment method, an arithmetic progression is formed by the number of tasks assigned to each priority level.

For example, If N are number of tasks and p is number of priority levels then

where ‘a’ tasks are assigned to highest priority level, ‘2a’ tasks are assigned to next highest priority level and so on.

3. Geometric Priority Assignment : In this assignment method, a geometric progression is formed by the number of tasks assigned to each priority level.

where ‘a’ tasks are assigned to highest priority level, ‘a^2’ tasks are assigned to next highest priority level and so on.

3. Logarithmic Priority Assignment : In this assignment method, shorter period tasks are allotted distinct priority levels as much as possible and lower priority tasks (tasks with higher period) are combined together and assigned to same priority level so that higher priority tasks would not be affected. For this the tasks are arranged in increasing order of their period.

If p max is the maximum period and p min is the minimum period among period of all tasks and p is the number of priority levels then,

and tasks with periods up to k are assigned to highest priority, tasks with periods from k to k^2 are assigned to next highest priority, tasks with periods from k^2 to k^3 are assigned to next highest priority and so on.

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How to Prioritize Tasks: Steps, Tips, and Techniques

By Kate Eby | August 26, 2021 (updated September 19, 2023)

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Task prioritization enables you to more effectively manage work tasks by identifying priority levels, based on urgency and importance. This article provides expert tips and best practices to help you prioritize your workload.

Included on this page, you’ll find expert tips and tricks for prioritizing work tasks, a variety of prioritization techniques , and a free priority matrix template that you can download to get started.

What Is Task Prioritization?

Task prioritization is the process of assigning order to tasks based on their urgency and importance. The goal is to improve your time management and productivity by focusing on and organizing the tasks you need to complete. 

Prioritizing tasks can often mean putting aside a task you want to do, and instead taking care of  harder or more challenging responsibility. Task prioritization is a process, and you can use several steps, techniques, and tips to help you effectively focus your attention and manage your time.

General Steps to Prioritizing Tasks

You can prioritize tasks by recognizing a number of factors involved with a single task. By understanding the priority of each task, you can then assign an order to determine when and how to accomplish each task.

• Make a List: Document the current outstanding tasks. Jot down any tasks or assignments that come to mind so that you can visualize the extent of the work awaiting you.
• Know the Deadlines: Add task deadlines to the list. Doing so allows you to more effectively separate your urgent tasks from less time-sensitive items. 
• Estimate the Time and Effort: By understanding the resources it takes to complete each task, you can more effectively identify the priority of a task and plan a course of action for tackling your list.
• Determine the Highest-Priority Tasks: Base this ranking on the tasks’ deadlines and estimated time to completion. Identifying the highest-priority tasks, based on urgency and importance, will give you clarity on what needs the most attention.
• Complete Highest-Priority Tasks First: Sometimes the hardest task is the one with the highest priority. By completing your highest-priority tasks over those you would prefer doing, you can increase efficiency in managing your priorities and time.
• Remain Flexible: The needs of your tasks may change when you least expect them — a deadline may be extended or shortened, or an assignment may suddenly be cut. It is important to remain flexible so that you can adjust task priorities and tackle your to-do list the most effectively.
• Review and Reevaluate Tasks Often: Keep track of your tasks and deadlines. Staying on top of your to-do list will help you complete priorities, ensure you don’t overlook important tasks, and remove tasks that are no longer urgent or important.

How to Prioritize Tasks at Work

In order to prioritize tasks at work, be aware of what you need to do and when you need to do it. You might find it difficult to execute on high-priority tasks when you prefer to work on other tasks. 

Phoebe Gavin , an author and productivity expert who helps people balance their lives through career, life, and productivity coaching, recommends the following:

• Set Deadlines for Every Task: Regardless of the size of the task, set a deadline. If a work item doesn’t already have a firm deadline, you may have to approach your colleagues for input. For example, a colleague might email you and ask you to review a presentation when you have time. Because there is no specified deadline, you may need to collaborate with your colleague to take into account the presentation’s due date and any other outstanding tasks on your list.
• Give Yourself Five Minutes of Willpower: Getting started is one of the greatest challenges to staying productive and focused. Starting on a challenging (or personally uninteresting) task is often daunting and can lead to procrastination. By assigning yourself five minutes to focus on the task, you will likely find that that five minutes will turn into more time, and you will make more progress and maybe complete the task altogether. For example, you might dread a lengthy assignment, and the due date is quickly approaching. Look at a blank document and decide, “I’m just going to work on this for five more minutes.” After focusing your attention for five minutes, you will likely find that you have already created a plan for your assignment and feel ready to complete more.
• Focus on One Task at a Time: Avoid multitasking, as this is not as effective or as productive as focusing on a single task. While multitasking might seem an effective use of time, you may ultimately waste your time by performing poorly on multiple tasks. Imagine that you are in a meeting, and you recognize an opportunity to respond to outstanding emails. As you write your emails, conversation in the meeting becomes indistinct background noise. Because of this, you miss a direct question from a colleague and have to be reminded to answer. You also realize, after the meeting, that one of the emails you sent was addressed to the wrong person and contained several errors that you have to fix and resend.
• Connect with Your “Why”: To prioritize your tasks, it is important to know your “why,” whether it’s the reason you hold a particular job, the goals you would like to achieve, or your personal values. The more you can connect with the tasks on your list and how they fit with your purpose and identity, the easier it will be to understand which of your tasks should rank higher on your to-do list. For example, imagine that you have started a new job and want to be seen as diligent and trustworthy among your boss and coworkers. You have a number of tasks to complete, including one you promised you would wrap up for a coworker. You choose to finish this task first, as doing so will fulfill your personal value of wanting to be seen as diligent and trustworthy.

Techniques for Task Prioritization

You can use many techniques for prioritizing tasks, such as creating a to-do list and limiting distractions, that can improve productivity and performance. The following techniques can help guide you through successfully organizing and completing your outstanding tasks.

Create a To-Do List

Create a to-do list that identifies and provides an overview of all your outstanding tasks. This will help you keep track of and visualize the tasks you need to do. Making a daily to-do list can be crucial for staying focused and meeting deadlines.

Example: You have what seems like a never-ending list of tasks floating around in your head. This list seems impossible to tackle, and you have to draw on your memory to recall the different requests and assignments. Organize your thoughts and jot down each task. By making a list, you realize there is an end to your tasks and can visualize your progress.

Check out one of these free task list templates to get a head start on your to-do list.

Eisenhower Priority Matrix

The Eisenhower Priority Matrix is a method that helps you choose which tasks should come first, based on urgency and importance. The matrix consists of four quadrants that are designed to put the priority level of a task into perspective. 

Example: You have a huge list of tasks to complete today and feel overwhelmed. Put each task through the Eisenhower Priority Matrix to identify the most important and urgent tasks. In doing so, you realize which tasks you need to address and plan out, and which tasks you need to assign to someone else. You may also find that you can completely remove some tasks.

Check out some of our priority matrix templates .

ABCDE Method

The ABCDE method assigns a letter value to each task: An A rating is the highest priority and needs to be addressed first, and an E rating is the lowest priority and can be addressed later.

Example: It’s Monday morning and you have a list of tasks that need your attention. One of those tasks is to create a short document, which you have continually pushed off for other assignments. But the deadline is approaching, and you have been asked to conduct a meeting, which will take a lot of preparation. The meeting will take place Friday, while the short document is due Tuesday. In using the ABCDE method, you assign a value of A to the short document, and a B to the meeting preparation, as it can be addressed later.

The chunking technique utilizes your time and productivity by identifying the similarities between the tasks on your to-do list. By chunking together similar tasks, you can get more work done quicker and more effectively, rather than by multitasking. This is a more focused and managed approach to completing the tasks on your to-do list.

Example: At work, you have a series of tasks you need to complete, and you receive additional requests from your coworkers and manager. Use the chunking method to write down every request and task. Review your list and identify similarities among tasks (e.g., those that involve client communication, such as messaging a client, calling another client, and setting up a meeting with a client). By chunking these tasks together, you are more focused and in the mindset of communicating with clients, and this makes completing the tasks quicker and easier.

1-3-5 Method

Using the 1-3-5 method , you reimagine your daily to-do list to help you take on tasks with clarity and focus. Start by jotting down nine tasks to complete for the day. Of these nine tasks, one should be a big task, three should be medium-sized tasks, and five should be small tasks. By organizing your to-do list using the 1-3-5 method, you can more effectively manage your time at work and meet the needs of your tasks.

Example: Lately, you feel disorganized. The tasks you complete seem unbalanced, and you feel like you are working on too many larger projects, while neglecting the smaller, equally important tasks. Using the 1-3-5 method, reprioritize your workday by deciding to limit your time on the larger projects to ensure you address tasks of all sizes. Rather than spending the whole day working on larger projects, set a schedule for the day that involves completing one larger project, three medium-sized tasks, and five smaller tasks. This method will help you feel less overwhelmed and more productive.

Getting Things Done (GTD) Method

The Getting Things Done (GTD) method , developed by productivity consultant David Allen, involves the following five pillars: capture, clarify, organize, reflect, and engage. Capture is the process of decluttering the tasks in your mind by writing them on a to-do list. Then, you clarify your tasks by breaking down larger tasks and discarding unimportant tasks. Then, organize the tasks on your list: Categorize and chunk tasks based on similarities and urgency. As you work, reflect on your lists frequently to update and revise the items. Lastly, engage directly with the tasks to complete them.

Example: You start a new role at work and are stressed by the different items you need to complete, which include onboarding tasks, studying all the areas of your new department, and meeting with several people for guidance. Use the GTD method to make headway on your to-do list. Start by writing down each task, and then breaking down some of the larger tasks into smaller, more manageable items. Following this, categorize the tasks on your list based on similarities. Because the role you are in is fast-paced and constantly changing, you will need to revise your list frequently to make sure your tasks reflect your needs. With a clear plan for your work, you are able to focus better on your tasks and complete them.

Eat the Frog

Eat the Frog refers to completing the most difficult and important task before all other work. This is most likely the task you dread most and often put off. By finishing your most daunting, highest-priority task first, you will likely improve your productivity.

Example: It is the beginning of the day and your colleague sends you a reminder to review a draft. You dread reading through the lengthy draft and would prefer to complete your smaller tasks. But you decide to tackle the review. After you complete the review, you are relieved (as is your colleague) and can continue the rest of your day with smaller tasks.

Know Your Distractions

Two main types of distractions can hinder your productivity. Environmental distractions are those that we can most easily control and can overcome with such simple actions as hiding your phone from your work area or decluttering your desk. Procrastination distractions occur when an environmental distraction becomes more prominent and serves as an unhealthy coping mechanism to dealing with stress. Procrastination prevents us from completing our work in an effective manner and can lead to more stress.

By first knowing — and limiting — your environmental distractions, you can prevent procrastination.

Example: You are trying to finish a report when your phone buzzes. Your phone sits right beside your computer, and you can see each notification. You stop working on your report to check a news notification, and suddenly find yourself scrolling through your phone for an extended period. Even when you set your phone down, you feel distracted from your report and can’t concentrate. Instead of continuing to scroll through posts, turn off your phone and place it out of sight. This allows you to concentrate on your work without distractions.

Warren Buffett’s Two-List Strategy

Warren Buffett’s two-list strategy involves jotting down 25 of the most important goals currently on your mind. Out of the 25 goals, circle five that you believe are the most critical in your life. These goals could cover your life, your career, or other areas. 

Using this list, make two lists:

• Most Important Goals: Highlights the goals most valuable to your life 
• Avoid At All Costs: Identify the goals that do not help you to accomplish your long-term priorities

Example: You have always had a long list of things you would like to accomplish in your life, many of which will take years of work. You want to go back to school to finish your MBA, work as a business professor, and move up in your current job to become a marketing director, but have also thought about a career in the medical field. At the same time, you also want to start a family and be involved as much as possible, among many other personal goals. Using Warren Buffett’s two-list strategy, you write out 25 of your major life goals. After determining your 25, you circle the five that most closely align with your larger goals. You circle getting your MBA, rising to a marketing director position, starting a family, saving for a good retirement, and working a four-day week to spend more time with family.  By doing this, you decide to spend less time, if any time at all, on the goals on the Avoid at All Costs list. This will free up time to help you move toward your five most important goals.

Ivy Lee Method

The Ivy Lee method requires you to prioritize your day by following a set of rules, which were developed by productivity consultant Ivy Lee more than 100 years ago. The rules of the Ivy Lee method are as follows:

• End every workday by writing down the six most important tasks you need to complete the next day.
• Order the six tasks by importance to ensure the highest-priority tasks are at the start of your to-do list for tomorrow.
• The next day, complete each of the tasks in order, if possible.
• Stay focused on each task, and avoid moving onto a different task before finishing the task at hand.
• At the end of the workday, develop a new list of six tasks to complete the next day. If you don’t complete a task, make sure to add it to the list for the next day.
• Repeat the process.

Example: You feel overwhelmed by the number of assignments and tasks at work. You need to complete a slide deck ASAP, prepare for a meeting with a customer that will take place in a few days, and research and gather participants for an upcoming conference, all while also writing several descriptions for new training content. You need to reevaluate your tasks and make sure you complete your most important priorities.

You try the Ivy Lee method and write out six tasks that you believe are most important and plan to complete tomorrow at work. You order the tasks based on their priority level. You assign completing the slide deck as your number one priority, researching participants as number two, reaching out to the participants as number three, and preparing for your meeting with the customer as number four. For the last two spots, you plan to focus on writing out two of the several descriptions you need to write. The next day at work, you follow each task, one by one, making sure not to multitask or move between tasks. At the end of the day, you find that you finished five of the six tasks and feel far more accomplished than you had the previous day. You create a new list of six tasks for the next day, making sure to include the description assignment you were not able to complete.

Expert Tips and Tricks for Task Prioritization

You can use many tips and tricks to help prioritize your tasks. Make the most of your time and accomplish the tasks on your list by implementing some of the following tips and tricks, as proposed by Phoebe Gavin:

• Promote Emotional Awareness: Emotions can sometimes be a barrier to completing what needs to be done. Recognize your emotions — both how you feel generally and how you feel toward the task at hand — to avoid coping mechanisms that lead to procrastination. Greater emotional awareness can help you create stronger and healthier coping mechanisms that help you focus on your tasks, even the most difficult ones.
• Break Down Large Tasks: Divide tasks into smaller, more manageable tasks in order to make them more approachable. Doing so also highlights progress on the larger task at hand. Learn more about managing large workloads by reading our comprehensive guide.
• Limit Environmental Distractions: Outside distractions can make completing a task feel impossible. Avoid distractions such as your phone, IM chat windows, email, and whatever else you might find yourself reaching for instead of the task at hand. By removing your environmental distractions, you can improve your focus on high-priority tasks.

Other useful expert tips and tricks include the following: 

• Use the Pareto Principle: The Pareto principle proposes that 80 percent of your results stem from 20 percent of your efforts. By keeping this principle in mind, you can more effectively prioritize your tasks to fit your goals and results.
• Adopt a Scheduling Tool: Doing so will help you keep track of your tasks and due dates to ensure that you don’t overlook any urgent or important tasks. Keeping your tasks organized will help you stay productive and prioritize your time. 
• Delegate Tasks: If your to-do lists are adding up and preventing you from spending time on what is important in your job, try delegating tasks for someone else to complete. This will help you free up time to work on the tasks that are most important in your role.

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• The Eisenhower Matrix: How to prioritiz ...

The Eisenhower Matrix: How to prioritize your to-do list

The Eisenhower Matrix is a task management tool that helps you organize and prioritize tasks by urgency and importance. Using the tool, you’ll divide your tasks into four boxes based on the tasks you’ll do first, the tasks you’ll schedule for later, the tasks you’ll delegate, and the tasks you’ll delete. In this piece, we’ll explain how to set up an Eisenhower Matrix and provide tips for task prioritization.

The Eisenhower Matrix is a task management tool that helps you distinguish between urgent and important tasks so you can establish an efficient workflow. In this article, we’ll explain how to set up an Eisenhower Matrix and provide tips for task prioritization .

What is the Eisenhower Matrix?

The Eisenhower Matrix is a way to organize tasks by urgency and importance, so you can effectively prioritize your most important work. 

Dwight D. Eisenhower—the 34th President of the United States and a five-star general during World War II—presented the idea that would later lead to the Eisenhower Matrix. In a 1954 speech, Eisenhower quoted an unnamed university president when he said, “I have two kinds of problems, the urgent and the important. The urgent are not important, and the important are never urgent.” 

Stephen Covey, author of The 7 Habits of Highly Effective People , took Eisenhower’s words and used them to develop the now-popular task management tool known as the Eisenhower Matrix. 

The Eisenhower Matrix is also known as the time management matrix, the Eisenhower Box, and the urgent-important matrix. This tool helps you divide your tasks into four categories: the tasks you’ll do first, the tasks you’ll schedule for later, the tasks you’ll delegate, and the tasks you’ll delete.

How to distinguish between urgent and important tasks

Urgent and important may seem like similar words, but when analyzing them in terms of the Eisenhower principle, the difference between the two is crucial. Differentiating between urgent and important within the Eisenhower Matrix can help you identify which tasks you should jump on and which tasks might be better handled by other team members. 

Urgent tasks require your immediate attention. When something is urgent, it must be done now, and there are clear consequences if you don’t complete these tasks within a certain timeline. These are tasks you can’t avoid, and the longer you delay these tasks, the more stress you’ll likely experience, which can lead to burnout .

Examples of urgent tasks may include:

Finishing a project with a last-minute due date

Handling an urgent client request

Fixing a busted pipe in your apartment

Important tasks may not require immediate attention, but these tasks help you achieve your long-term goals . Just because these tasks are less urgent doesn’t mean they don’t matter. You’ll need to thoughtfully plan for these tasks so you can use your resources efficiently. 

Examples of important tasks may include:

Planning a long-term project

Professional networking to build a client base

Regular chores and maintenance projects

Once you know how to distinguish between urgent and important tasks, you can begin separating your tasks into the four quadrants of the Eisenhower Matrix. 

The four quadrants of the Eisenhower Matrix

A long to-do list of tasks can feel overwhelming, but the goal of the Eisenhower Matrix is to go through these tasks one by one and separate them by quadrant. Once you can see your tasks in their designated categories, you’ll be able to schedule them  and accomplish your most important work. 

Quadrant 1: Do

Quadrant one is the “do” quadrant, and this is where you’ll place any tasks that are both urgent and important. When you see a task on your to-do list that must be done now, has clear consequences, and affects your long-term goals, place it in this quadrant. 

There should be no question about which tasks fall into this quadrant, because these are the tasks that are at the front of your mind and are likely stressing you out the most. 

Quadrant 2: Schedule

Quadrant two is the “schedule” quadrant, and this is where you’ll place any tasks that are not urgent but are still important. Because these tasks affect your long-term goals but don’t need to be done right away, you can schedule these tasks for later. 

You’ll tackle these tasks right after you tackle the tasks in quadrant one. You can use various time management tips to help you accomplish the tasks in this quadrant. Some helpful strategies may include the Pareto principle or the Pomodoro method . 

Quadrant 3: Delegate

Quadrant three is the “delegate” quadrant, and this is where you’ll place any tasks that are urgent but not important. These tasks must be completed now, but they don’t affect your long-term goals. 

Because you don’t have a personal attachment to these tasks and they likely don’t require your specific skill set to complete, you can delegate these tasks to other members of your team. Delegating tasks is one of the most efficient ways to manage your workload and give your team the opportunity to expand their skill set.

Quadrant 4: Delete

Once you’ve gone through your to-do list and added tasks to the first three quadrants, you’ll notice that a handful of tasks are left over. The tasks left over are tasks that weren’t urgent or important. 

These unimportant, non-urgent distractions are simply getting in the way of you accomplishing your goals. Place these remaining items on your to-do list in the fourth quadrant, which is the “delete” quadrant. 

4 tips for prioritizing your tasks

The best way to understand the difference between urgency and importance is to use the Eisenhower Matrix, but you may still find yourself struggling to prioritize your tasks. Here are some tips that can help you with prioritization as you sort your tasks in each quadrant.  

1. Color-code your tasks

Color-coding your tasks is a tactic that can help you visualize high-priority items. As you go through your to-do list tool , try giving yourself four colors based on level of priority. Use the code as follows:

Green = Highest priority items

Yellow = Second-highest priority

Blue = Third-highest priority

Red = Not a priority

Once you’ve labeled your tasks by color, these colors will directly translate to your Eisenhower Matrix. Your green tasks are your “do” tasks for quadrant one. Your yellow tasks are your “schedule” tasks for quadrant two. Your blue tasks are your “delegate” tasks for quadrant three, and your red tasks are your “delete” tasks for quadrant four.

2. Limit tasks to 10 per quadrant

Even if you have a lot of tasks on your to-do list, try to limit your tasks to 10 items per quadrant. This will keep your Eisenhower Matrix from becoming cluttered and overwhelming. 

You can make multiple matrices, but limiting your task list to necessary action items will ensure you’re beginning the prioritization process with no time to waste.

3. Make personal and professional to-do lists

Another way to limit the number of items on your Eisenhower Matrix is to create separate matrices for your personal and professional to-do lists. 

Your work and personal tasks require different timelines, resources, and methods, and they’ll likely require different thought processes as well. In order to effectively manage your personal and professional goals , you’ll need to divide and conquer. 

4. Eliminate, then prioritize

Eliminate unnecessary tasks first to effectively prioritize. With this strategy, you’ll address quadrant four before moving on to quadrants one, two, and three. 

As you skim through your to-do list, assess what items you’ve written down that don’t need to be there. 

In fact, 60% of our time at work is spent on work about work—things like sharing status approvals or following up on information. If you can quickly scratch off items, go ahead and do so. This will speed up the prioritization process, and you’ll likely go through a second round of elimination on the back end.

Eisenhower Matrix example

To get a better understanding of what tasks you may place in each quadrant of your Eisenhower Matrix, we’ve gone ahead and provided some examples for you here.

Examples of tasks you may include in quadrant 1:

Write a blog post due tomorrow

Finish a project proposal

Respond to client emails

Examples of tasks you may include in quadrant 2:

Sign up for a professional development course

Attend a networking event

Add improvements to a personal project

Examples of tasks you may include in quadrant 3:

Upload blog posts

Transcribing meeting notes

Fielding non-client emails

Examples of tasks you may include in quadrant 4:

Work about work

Attending a status meeting

Sharing status approvals

Remember that it’s best to have separate matrices for work and home life so you can tackle your to-do lists using methods best suited for the time and place.

Build your Eisenhower Matrix with task management tools

Sorting through your to-do list is the hardest part of the Eisenhower Matrix, but with automation, you no longer need to do this step manually.

Use task management software to determine which of your tasks are of highest priority. With task management, you can categorize, color-code, and delegate tasks to your team. Let the Eisenhower Matrix increase your productivity so you can achieve your goals in less time.

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Eisenhower Matrix: How to prioritize tasks (examples, template)

In this guide, we’ll introduce you to the Eisenhower Decision Matrix and show you how to use it to prioritize your team’s time and tasks.

We’ll show you some Eisenhower Matrix examples and provide a free, customizable template you can use when implementing the approach on your team.

What is the Eisenhower Matrix?

The Eisenhower Matrix, also known as the Eisenhower Decision Matrix, Eisenhower Box, and Urgent-Important Matrix, is a time and task management tool that helps individuals prioritize their tasks by considering two factors: urgency and importance.

It is named after Dwight D. Eisenhower, the 34th President of the U.S., who was known for his effective time management strategies.

To use the Eisenhower Matrix, first identify all the tasks you need to complete. Then, based on the urgency and importance of each one, place it in one of the four quadrants of the matrix:

Here’s what each quadrant of the Eisenhower Matrix represents:

• Urgent and important — Tasks that you need to complete as soon as possible. These are your top priority
• Not urgent but important — Tasks that are critical but don’t have a pressing deadline. You should schedule time for them, otherwise, they might become urgent
• Urgent but not important — Tasks that are pressing but not important. If possible, you should delegate them
• Not urgent and not important — Tasks that are neither urgent nor important. You should eliminate them if possible

Who uses the Eisenhower Matrix?

Anyone who needs to manage their work can use this approach. It is particularly useful for project managers because it helps them focus on the most important tasks and avoid being overwhelmed by urgent but unimportant tasks.

If you’re a product manager or a project manager, you might use the Eisenhower Matrix to do things such as:

• Identify and prioritize project tasks — Determine what tasks need to be completed for a and place them in the appropriate quadrant based on their level of urgency and importance. This will help you focus on the most important tasks first
• Delegate tasks — Identify tasks that are urgent but not important and delegate them to team members or other resources if possible. This will free up your time to focus on more important tasks
• Manage project resources — Identify tasks that are important but not urgent and schedule them in advance to ensure that they are completed on time. This will help you manage your resources effectively and avoid last-minute rushes to complete tasks
• Monitor progress — Track your progress on tasks and identify any bottlenecks or roadblocks. This will help you stay on track and ensure that your project stays on schedule

By using the Eisenhower Matrix, you can effectively manage your time and resources and ensure that projects are completed on time and within budget.

Eisenhower Matrix example

Let’s see an example of how the Eisenhower Matrix works in practice.

In this example, the project manager has identified the following tasks:

• Write project proposal (quadrant 1)
• Develop project plan (quadrant 1)
• Conduct market research (quadrant 2)
• Attend team meeting (quadrant 3)
• Write a report on competitor analysis (quadrant 4)

Based on the level of urgency and importance of each task, the project manager has placed them in the appropriate quadrant of the matrix:

Here’s how you can read the example Eisenhower Matrix above:

• The project manager should focus on the tasks in quadrant 1 first because they are both urgent and important
• The task in quadrant 2 also warrants attention because it is important but not urgent
• The task in quadrant 3 can be delegated or postponed because it is urgent but not important
• The task in quadrant 4 can be eliminated, as it is neither urgent nor important

Eisenhower Matrix template

If you want to give the Eisenhower Decision Matrix a try, you can use our free, customizable template to get started.

To use the Eisenhower Matrix template , click here and then select File > Make a copy from the menu above the spreadsheet.

How to categorize your tasks

Categorizing tasks using the Eisenhower Matrix can be a helpful way to prioritize your tasks and manage your time effectively. To realistically categorize your tasks, it is important to be honest with yourself about the level of importance and urgency of each task.

Before you begin categorizing your tasks, take some time to think about what is important to you and your goals. This will help you prioritize tasks that align with your values and objectives.

Think about the potential consequences of not completing a task. If the task is important, there will likely be negative consequences if it is not completed. On the other hand, if the task is not important, the consequences of not completing it may be minimal.

Establish deadlines for each task to help determine its level of urgency. A task with an imminent deadline is likely to be more urgent than a task with a longer timeline.

It is important to be consistent in your categorization of tasks. If you consistently categorize tasks based on their level of importance and urgency, you will be better able to prioritize your time and efforts.

From time to time, review and update your categorization of tasks, as priorities can change over time. Make sure to reassess the level of importance and urgency of your tasks regularly to ensure that you are focusing on the most important tasks.

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How to get your team using the Eisenhower Matrix

If you plan to use the Eisenhower Matrix, you first need to explain the concept of the Eisenhower Matrix to your team and how it can be used to prioritize work and manage time efficiently.

Have each team member identify all the tasks they need to complete, including both work-related and personal tasks.

Then, have team members categorize their tasks using the matrix, placing each task in the appropriate quadrant based on its level of importance and urgency.

Encourage team members to focus on tasks in quadrant 1 first because they are both urgent and important. They should also give attention to tasks in quadrant 2, which are important but not urgent. Tasks in quadrant 3 can be delegated or postponed, while tasks in quadrant 4 should be eliminated if possible.

Encourage team members to regularly review and update their task priorities. This will help them stay on track and ensure that they are focusing on the most important tasks.

Limitations

Like any other framework or tool, the Eisenhower Matrix is not a one-size-fits-all solution and has some limitations.

For one, categorization of tasks into the quadrants of the matrix can be subjective because the level of importance and urgency of tasks can vary depending on the individual and the context. This can make it difficult to determine the appropriate categorization of certain tasks.

Furthermore, the Eisenhower Matrix does not consider external factors such as the workload or the availability of resources. It also does not account for unexpected events or priorities that may change during the day.

Alternative prioritization and task management frameworks

There are many different frameworks and tools that can be used to prioritize tasks and manage time effectively. Here is how the Eisenhower Matrix compares with other popular prioritization frameworks:

Priority Matrix

Time management matrix, pareto principle (80/20 rule).

The Priority Matrix is a tool that helps individuals prioritize tasks based on their potential impact and the resources required to complete them. It consists of a matrix with four quadrants, similar to the Eisenhower Matrix. However, the criteria used to categorize tasks are different.

The Time Management Matrix , also known as the Covey Matrix, is a tool developed by Stephen Covey to help individuals prioritize tasks based on their level of importance and urgency. It consists of four quadrants similar to the Eisenhower Matrix, but the criteria used to categorize tasks are slightly different.

The Pareto Principle, also known as the 80/20 rule , states that 80 percent of the effects come from 20 percent of the causes.

This principle can be used to prioritize tasks by focusing on the 20 percent of tasks that are most important and will have the greatest impact.

Kanban is a project management method that uses a visual board to track the progress of tasks through various stages of completion. It is a flexible tool that can be used to prioritize tasks by moving them to the top of the board or assigning them to specific team members.

The Eisenhower Matrix can be a valuable tool for managing time and priorities efficiently and achieving your goals, whether you are an individual or part of a team.

By understanding the framework and how to implement it, you can take control of your time and be more productive.

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How to Prioritize Tasks Like a Pro: The Ultimate Guide

Prioritizing tasks is a crucial skill for anyone looking to be productive and get things done. A pro approach to task prioritization involves:

• Understanding the value of each task.
• Setting clear goals.
• Breaking down large tasks into smaller ones.
• Using prioritization techniques.
• Adjusting their to-do list.

Using these strategies, you can ensure that you focus on the most important tasks and make the most of your time and resources. Additionally, prioritizing tasks can help you manage stress, improve motivation, and increase your overall sense of accomplishment.

What Is Prioritizing?

Prioritizing in project management refers to evaluating and ranking tasks, resources, and goals based on their level of importance or urgency concerning the project's overall success. This allows project managers to allocate resources, schedule activities, and make decisions to help them achieve their project goals more efficiently and effectively.

Prioritizing tasks can be done using techniques such as the Eisenhower Matrix , the MoSCoW method , and critical path analysis.  Effective prioritization helps project managers stay focused, manage constraints, and make trade-off decisions that ultimately contribute to project success.

How to Prioritize Work Tasks?

• Identify all tasks: Write down all the tasks you must complete, including deadlines and dependencies.
• Evaluate the importance and urgency of each task: Consider the impact and urgency of each task, and assign a priority level such as high, medium, or low.
• Use a prioritization matrix: Tools like the Eisenhower Matrix (urgency vs. importance) can help you prioritize tasks more effectively.
• Break down larger tasks: Divide larger tasks into smaller, manageable components and prioritize those.
• Set realistic goals: Be realistic about how much time you have and how much work you can complete, and adjust your priorities accordingly.
• Regularly review and adjust: Regularly reassess and adjust your priorities as needed to stay on track and focused on the most important tasks.

In addition to already mentioned aspects, we would like to cover several more things that would take your prioritization skills to another level:

• Mindfulness and priority setting: Mindfulness helps your team stop wasting time on things they can't change and focus on actions that make a difference.
• Managing distractions and stress management: You should switch off distractions like email pings, Slack messages, or social media updates. While there will be projects that won't thrill you, keep motivation and focus on things that matter.
• Goal setting: SMART goals are Specific, Measurable, Achievable, Relevant, and Time-bound. To set SMART goals, make sure each goal is clearly defined and specific, includes measurable criteria, is achievable with the available resources, aligns with personal or organizational priorities, and has a set deadline.
• Overcoming procrastination : To overcome procrastination, set clear goals, break tasks into smaller parts, use a schedule, eliminate distractions, hold yourself accountable, and reward everyone for completing tasks.

Create a Task List - A Master List of All Your Tasks

Creating a task list or "Master List" of all your tasks is helpful for managing and prioritizing your workload. Here's how you can create one:

• Brainstorm : Write down all the tasks you need to complete, whether big or small.
• Categorize: Group similar tasks together, such as work tasks, personal tasks, or errands.
• Prioritize: Assign a priority level to each task, such as high, medium, or low.
• Make it simple: Break it down by monthly, weekly, and daily goals.
• Schedule: Add deadlines and due dates to your tasks.
• Review: Regularly review your task list to see what has been completed, what needs to be rescheduled, and what can be deleted or deferred.
• Update: Keep your task list updated as new tasks arise or priorities change.

Having a Master List of all your tasks can help you stay organized, focused, and on top of your workload. ActiveCollab offers a feature that automatically gathers all the tasks assigned to you into a single view. This master checklist helps you stay on top of your work and organize and prioritize tasks based on their due date.

Important and Urgent Priorities

Important and urgent priorities are part of Eisenhower's Principle. To minimize many tight deadlines, you must distinguish between these two.

• Important: These activities have an outcome that leads us to accomplish our goals, whether they are personal or professional.
• Urgent: When it comes to urgent activities, they require immediate attention and are usually linked to achieving someone else's goals. They demand attention because of the immediate consequences.

Eisenhower's principle boils down to this: The urgent are not important, and the important are never urgent.

Adopt Task Prioritization Methods

Task prioritization methods are techniques used to rank tasks or projects in order of importance. Here are some common ones:

• Urgency and Importance Matrix: Ranks tasks based on both the level of urgency and the level of importance.
• Eisenhower Matrix: Classifies tasks into four categories based on urgency and importance.
• ABCDE Analysis : This prioritization technique categorizes project tasks or deliverables based on their relative importance and urgency. It assigns a priority designation of "A" for high-priority items that must be completed quickly, "B" for items that are important but not urgent, "C" for tasks that are of lower importance, "D" for tasks that can be deferred or delegated, and "E" for tasks that can be eliminated. This helps project managers prioritize their workload and allocate resources effectively. You can use ActiveCollab to manage tasks and design fields in which you will enter additional information, like the letter grade if you use the ABCDE method, urgency and importance for Eisenhower Decision Matrix, or priority level if you are doing "eat the frog."
• The Pomodoro Technique : This time management method breaks down your work into 25-minute intervals, called "pomodoros." After each "pomodoro" comes a short break. It helps increase focus and productivity by reducing distractions and allowing regular breaks.
• Pareto Principle: 80/20 rule states that 80% of results come from 20% of the effort.
• MoSCoW Method: Ranks tasks based on their priority as Must-Have, Should-Have, Could-Have, or Won't-Have.
• Time Management Matrix: Focuses on the time required to complete each task.
• Critical Path Analysis: A project management technique that identifies the tasks that must be completed on time to complete a project as planned.

Evaluate the Importance of Your Tasks

If you know the proper steps, it's pretty easy to distinguish important from less relevant tasks:

• Order tasks by estimated effort: If a particular task seems to be equivalent in importance, try to figure out which one demands the most effort and start on that one.
• Tackle your most intense: Put your most tedious and daunting tasks first, also known as " eating the frog ." Your high–effort task should come first.
• Focus on one task at a time, and try not to multitask: multitasking might seem right. If you try to fit too many tasks into your schedule, it's a recipe for burnout.

Maintaining Flexibility and Adaptability in Prioritization

• Sunk cost fallacy: As you go through prioritization exercises, it's crucial to remember to be flexible. You can hardly know what the future holds, and planning and prioritizing are really just guessing.
• Stick to a schedule that reflects your priority tasks: You could map out an ideal daily and weekly schedule following your other obligations.
• Use a calendar tool to schedule your tasks: Using a calendar tool allows you to balance high-effort and high-priority tasks, meet deadlines, avoid scheduling conflicts, manage workload, and maintain a work-life balance. We suggest using the Calendar view in ActiveCollab and spotting open slots or days when your schedule might be overloaded in order to spread your work more evenly.

Prioritize Time Management

• Use Time Multipliers to make the most of your daily hours: These are the tools or strategies that create more time for you in the future.
• Ample time for any given task: If you are constantly going against deadlines, then you may be the one to blame for it. Ask yourself how long it takes to complete your day-to-day tasks and whether your estimates are made on actual data.
• Creating a time management system: To manage time properly , you should include a daily schedule, prioritize tasks, and eliminate time-wasters.
• Time tracking : It would be a good idea to use several time-tracking apps or software which can help you get a sense of how long it takes to complete a particular task or identify areas where time is wasted.
• Time blocking: This time management method demands you to split your daily schedule into blocks of time. The key is to prioritize your task list in advance and review tasks you didn't finish at the end of your workday.

Recognizing the Feasibility of Task Accomplishment

Feasibility of task accomplishment refers to evaluating the potential success of completing a task based on factors such as resources, time, skills, and constraints. To recognize the feasibility of a task, you should consider the available resources, the time frame for completion, the skills and expertise required, and any potential constraints or obstacles that may impact the task.

Feasibility analysis can help determine if a task is realistic and achievable, allowing individuals and organizations to decide whether to proceed with the task or explore alternative options. It is important to acknowledge what you can do and how much work you can fit into your daily schedule and review your work constantly.

Balancing Priorities Through Limits and Task Cutting

Balancing priorities means prioritizing tasks and assigning values to them based on their importance and urgency. Limits and task-cutting are strategies used to balance priorities and help individuals and organizations achieve their goals.

Limits refer to setting boundaries or constraints around a task's time, resources, or scope. For example, setting a deadline or limiting the budget for a project can help focus efforts and prioritize tasks.

Task cutting involves reducing the scope of a task or eliminating it if it is not deemed necessary or feasible. Task cutting is often used when there is insufficient time or resources to complete all the tasks on a list. This strategy helps individuals and organizations focus on the most important tasks and prioritize their efforts accordingly.

Prioritizing Tasks in a Collaborative Environment

Prioritizing tasks in a collaborative environment requires clear communication, shared goals, and a common understanding of the priorities among team members. Here are a few strategies which can help you prioritize tasks:

• Define shared goals: A clear understanding of the team's goals and objectives can help prioritize tasks and decide what tasks are most important.
• Assign roles and responsibilities: Defining roles and responsibilities can help ensure that tasks are distributed effectively among team members, and that everyone knows what to focus on.
• Communicate regularly: Regular communication and updates can help team members stay informed about what others are working on, identify potential roadblocks, and prioritize tasks accordingly. It's also important to delegate tasks effectively and find the right individual. This person needs to be someone whom you already trust, someone who is competent and who is available in time of need.
• Regularly reassess priorities: A team's priorities can change as projects progress, so it is important to reassess priorities and make necessary adjustments regularly.

Prioritizing Tasks Improves Productivity

A common reason why people are unproductive is that their working day needs more structure. Most of the time, tasks are treated as both business-critical and urgent, which needs to be corrected and prevent team members from managing their workload effectively.

Task prioritizing helps project managers and their teams break down their lists into manageable chunks. As you learn what things work best for your team, there are a couple of things to keep in mind:

• Your goal is to get things done
• Use different productivity methods

As mentioned earlier, you can apply several productivity methods to your projects and mix and match them to identify your ideal combination.

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10 Free Prioritization Templates to Organize Your Team’s Tasks

ClickUp Contributor

February 13, 2024

Picture a bustling intersection during rush hour. Hundreds of cars are lined up, and every driver is in a hurry to get to work on time. Without proper traffic regulations, complete chaos would ensue. 🚨

Prioritization tackles similar issues, but now we’re talking about looming deadlines and demanding clients instead of traffic jams. It provides guidelines for efficient organization when simultaneously juggling many tasks with different levels of urgency. 

To master your prioritization game, you must find a proper tool for the job. Check out the list of our favorite ClickUp priorities templates, and choose your sidekick! We’ll explore what each brings to the table and how you can use it to push productivity .

What is a Priorities Template?

What makes a good priorities template, 1. clickup project prioritization matrix template, 2. clickup simple priority matrix template, 3. clickup action priority matrix template, 4. clickup 2x2 priority matrix whiteboard template, 5. clickup prioritization whiteboard template, 6. clickup daily action plan template, 7. clickup smart action plan template, 8. clickup backlogs & sprints template, 9. clickup start-stop-continue template, 10. clickup simple to-dos template, top priorities templates: an overview.

Priorities templates are frameworks pre-built to help you organize assignments based on their urgency and business-impacting levels.

With all your tasks sorted, you and your team can work faster and with confidence. You know exactly what, when, and why needs to be done, so your efforts are purposeful and aligned with the company’s and team’s objectives. 

Using a project prioritization template has many benefits, including:

• Enhanced focus, efficiency, and productivity 
• Improved strategic decision making
• Effective resource allocation
• Reduced stress and better work-life balance
• Deadline adherence and timely goal achievement

Like prioritization techniques, priorities templates come in many forms with different criteria for evaluating tasks and can be used in various settings by people in specific organizational positions.

In general, an effective priorities template should check the following boxes:

• Visualization : Has a clear structure and an appealing design with attractive colors and elements to help bring your plans to life 
• Accessibility : Enables you to access and edit it from any device, allowing planning on the go
• Intuitiveness : Features a clean interface and is simple to use for all team members, new or experienced
• Flexibility : Accommodates different types of projects and teams and allows you to scale up as the business grows
• Integration : Supports integration with your favorite project management tools, allowing you to optimize the workflow
• Collaboration : Enables everyone from your team to participate in prioritization, offering advanced sharing, user control, and communication features

10 Project Prioritization Templates to Use in 2024

A sound task prioritization plan can set the stage for upcoming work so multiple projects run smoothly. By putting in the extra effort beforehand, you can save time during more demanding production stages—preventing miscommunication, delays, errors, and other mishaps that erode client trust. ⏰

Not sure how and where to start? Use one of these 10 ClickUp templates and optimize your work prioritization process in no time . They’re free to use, so you can explore different options with no strings attached!

The ClickUp Prioritization Matrix Template is an excellent way to visualize the complex hierarchy of upcoming tasks . 

It’s a 3×3 matrix with two axes representing Impact and Effort variables. A task can score low, medium, or high on these variables. The cells are strategically color-coded to help you assess the overall priority of assignments at a glance:

• Red : Do now 
• Orange : Do next
• Green : Do last

To add content to a ClickUp Whiteboard, create a new sticky note somewhere on the board and briefly describe the task. After assessing its impact and effort levels, drag and drop it to the cell that best represents its priority status. You can use the exact positioning of the note within the cell to introduce an additional ranking layer.

Being in Whiteboard view, the template allows freedom and creativity. You can add images and videos, link to tasks and docs, and doodle to express ideas to your team with a fun flare. 💥

If you prefer to stick with the tried-and-true Eisenhower priority matrix , the Simple Priority Matrix Template by ClickUp might be the tool to prioritize projects.

The template has a 2×2 structure and includes Urgency and Importance variables. Like the previous addition to our list, you can use the framework by adding sticky notes to its four cells. 

Depending on their positions, tasks can be classified as:

• High importance, high urgency: Do First
• Low importance, high urgency: Do Next
• High importance, low urgency: Do Later
• Low importance, low urgency: Do Last

Did you know you can use ClickUp to handle nearly all your behind-the-scenes operations, not only prioritization? 

Turn the sticky notes into tasks with one click and manage them all in List view. You can assess employee capacity, assign tasks, and track progress or add subtasks, checklists, attachments, and anything else aiding in task completion.

Another 2×2 matrix, the ClickUp Action Priority Matrix Template allows project managers to assess tasks based on their required effort and potential impact . 

Before deciding on their priority, list all tasks in the gray rectangle on the right side of the template using sticky notes. Feel free to change their colors to introduce additional information. For example, the color can indicate the assignee, department, or another evaluation factor. 

You can organize the tasks across four different cells, which have clever names:

• Big Projects : High impact + high effort
• Quick wins : High impact + low effort
• Fill-ins : Low impact + high effort
• Thankless tasks: Low impact + low effort

As with other ClickUp Whiteboard templates, you can customize almost every element to perfection. Feel free to go where your imagination takes you with images, videos, doodles, and diagrams. 🎨

As its name suggests, the ClickUp 2X2 Priority Matrix Whiteboard Template is a standard 2×2 matrix that you can drag and drop tasks in. 

Despite its familiar feel, the template has a unique approach compared to the previous recommendations on our list. Besides Importance, it includes Priority as one of the variables. This kind of structure is helpful when the task’s priority has been determined, but it doesn’t align with its overall significance.

While a task may be labeled as a high priority, that doesn’t mean it should be your primary concern. Use this template to figure out where you should allocate your time, considering the broader context.

The Prioritization Whiteboard Template by ClickUp provides a colorful and innovative way to manage tasks, ideas, and proposals within your organization. 🌈

Although similar to a matrix with its two axes, the template moves away from the strict categories and introduces a more flexible layer system. Depending on where you position them on the diagram, tasks can have the following statuses:

• Must Be Done/Feasible : Highly attainable and highly significant
• Great To Have/Needs Review : Highly attainable or highly significant
• Won’t Work/Impossible : Hardly significant or hardly attainable

Start by listing all your tasks as sticky notes in the Idea Pool section on the right. This prioritization template allows you to engage employees cross-functionally by assigning a note color to each department. After creating the list, assess the Significance and Attainability of tasks and move them to their respective positions.

Remember to update the legend on the left to help others get around the diagram more easily ! 🧭

We’re moving away from the simple project prioritization matrix template structure to a more comprehensive and interactive one. The ClickUp Daily Action Plan Template provides all the tools you need to boost productivity and efficiently reach your daily team goals . 📅

With this template, you can:

• Centralize all data to make it easily accessible to everyone
• Organize tasks and track their progress 
• Schedule and assign tasks and get a holistic view of the project timeline

The first thing you should do is define the high-end goal. You can do so in the small table at the top of the template’s List view. Next, define the tasks and subtasks for each department. Use the columns on the right side to set the deadline, indicate the task’s complexity, or introduce a custom field with other relevant information.

If you’re a fan of Kanban boards, you may find it more convenient to organize tasks in Board view. Whatever the layout, feel free to customize the grouping, filters, and other elements to tailor the template to your team’s needs. 

Thanks to the Timeline view, you can effectively manage time . By defining task dependencies, ensure tasks always follow the most logical and efficient order, even in the case of frequent rescheduling.

The SMART Action Plan Template by ClickUp gives you an opportunity to analyze your goals in-depth . You can specify the means of achieving them and measuring success. 📐

Since you’ve probably worked in Word, Google Docs, or a similar program, this template should ring a few bells! 🔔

Write down your thoughts in the designated text boxes, delete the banners, and you’re good to go.

Begin by defining your ultimate goal . In the following section, address the specific aspects of your action plan, following the SMART principle:

• Specific : What do you have to do to accomplish the goal? Who are the other individuals responsible, and what are their duties?
• Measurable : Which metrics will you use to track the progress and determine the success of your efforts? Are there any milestones that you must hit?
• Achievable : How do you plan on achieving the goal? What tools do you need? Which skills or knowledge do you have to brush up on?
• Relevant : Why does this goal matter? How does it fit in with the bigger picture and the company’s objectives?
• Time-bound : Are there any pre-set deadlines that you need to abide by? If not, how much time do you think you need to reach your target?

In the Follow-Through table at the end, report on your progress after pre-set time intervals. Outline key accomplishments, shortcomings, and areas that require additional support.

Software development team leads, listen up—this template is for you! The ClickUp Backlogs & Sprints Template is a comprehensive productivity tool based on Scrum and Agile methodologies . 

It occupies an entire ClickUp Space and offers various views and features, allowing you to manage the entire process from a single app. It might be challenging for beginners, but the included instructional Doc should provide enough guidance.

The template comes with several views, including:

• Epic List for a broad overview of all user stories
• Backlog Prioritization List to help you organize user stories and bugs 
• Sprint List and Daily Standup Board for long-term planning
• New Epic Request Form to collect user feedback
• Calendar, Timeline, and Workload views for scheduling 
• Retrospectives Docs to summarize and get input from teams

When it comes to priority management , the Backlog Prioritization List offers plenty of custom columns to help you evaluate tasks. You can assign Sprint Points, use the MoSCoW sorting method, appoint a priority tag, or introduce your own project criteria.

From time to time, you should re-evaluate your workflows to identify areas for improvement. After all, the task order doesn’t matter much if your processes are outdated and ineffective at their core. 

The ClickUp Start-Stop-Continue Template provides a framework for optimizing processes within your team or organization . It’s another Whiteboard template, so customization options are high.

First, establish the primary goal, i.e., what you wish to achieve. For example, that could be improving customer service or streamlining the onboarding process.

Create a list of tasks involved in the said process using sticky notes. Through discussion with your teammates or stakeholders, decide which category each task belongs to:

• Start : Tasks that you’re not doing but should be
• Stop : Ineffective tasks that take up a lot of time but bring little business value
• Continue : Tasks that have proven to be impactful and should remain in the process

Drag and drop the notes symbolizing tasks into their appropriate positions. You can turn the plans into action by creating tasks in List view, unlocking many other functionalities. Make sure to assess and upgrade the process again after a certain period to ensure its relevance.

In some cases, the simplest solution is the most effective one. The Simple To-Dos Template by ClickUp is a perfect example of that. It provides a straightforward way to manage tasks without unnecessary complexities or distractions .

Use the List view as your master task list. By default, the columns show the assignee, due date, priority tag, comments, and more. The status column features a dropdown menu with customizable categories. 

You can personalize the columns as well, choosing between 20 field options, including:

• Progress bar

Expand the information by adding subtasks, checklists, and attachments. Feel free to explore other views, such as Board and Gantt, and play around with the filters to find the optimal display variant. 

Not sure which template to try? Check out the following table for a summary of all options:

Supercharge Your Productivity With Priorities Templates

A solid prioritization system boosts your team’s performance, leading to faster and higher-quality output. It also lays the foundation for future work, allowing for efficient medium- and long-term planning. Task by task, project by project—your team can become one of the company’s top performers.

By using one of these awesome priorities templates, you can get a headstart and reach your productivity targets in no time! 🏁

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How to prioritize tasks when everything feels important

Here are 10 prioritization techniques to help you identify your most important work..

There's never enough time to do everything you want to do. It's a universal problem. And we all accept that fact and live with its reality until the worst-case scenario rears its ugly head: there's not enough time to do everything you have to do.

When everything on your to-do list feels like it's of crucial importance (or when someone you answer to feels that way), use one or more of these prioritization techniques to help you identify your most important task and get work done.

What is a prioritization technique?

A prioritization technique helps you make informed decisions about the order you should complete your tasks based on different factors like their importance and due dates. With a list of prioritized tasks in hand, you have a shield to respectfully push back against unnecessary meeting invites and last-minute requests. 

For example, when stakeholders would approach my teammates with "urgent" requests, we would show them our prioritized task list and ask, "What should we cut in order to accommodate this request?" After seeing the importance of the other things on the list, urgent requests often suddenly became much less urgent.

But prioritization techniques aren't limited to regaining control over workplace tasks. They're also useful for managing competing priorities from your family, friends, and even that part of your brain that's always on the lookout for side projects.

How to prioritize tasks: 10 prioritization techniques

Priority matrix  

MoSCoW prioritization method

ABCDE method

Scrum prioritization

Bubble sort method

Most Important Task (MIT) method

The Ivy Lee method

1-3-9 prioritization technique  

Two lists technique

Pareto principle (80/20 rule)

1. Priority matrix

The priority matrix technique consists of distributing your tasks across a four-quadrant matrix like the one shown below. The x-axis represents one value, and the y-axis represents another. Each quadrant, then, represents priority based on the defined values.

There are infinite ways you could organize your priority matrix. Here are popular priority matrix examples to get you started.

Eisenhower matrix

With the Eisenhower matrix, the x-axis represents urgency while the y-axis represents importance , leaving you with the following categories (from the top-left going clockwise):

Important but not urgent tasks are lower priorities that you should add to your to-do list for later.

Important and urgent tasks are your top priorities.

Urgent but not important tasks are ones that must get done but can be delegated to someone else. 

Not urgent or important tasks are probably unnecessary distractions. 

To use it, evaluate each task based on its urgency and importance, and then place the task in the corresponding quadrant.

By placing each task on your list into a quadrant on the Eisenhower Matrix, you can determine what needs to be done now, what you can save for later, what's worth delegating, and what you can scratch out altogether.

Impact effort matrix 

In the impact-effort matrix, the x-axis represents impact, and the y-axis represents effort . So you'll have a matrix with the following quadrants (from top-left going clockwise):

High effort, low impact 

High effort, high impact 

Low effort, high impact 

Low effort, low impact 

To distribute your tasks accordingly, evaluate how much effort each one will take and the impact completing it will have. Any tasks in your Low effort, high impact quadrant are your top priorities, followed by ones in your High effort, high impact quadrants. 

If you have a lot of tasks in these two quadrants, consider working on one or two from your Low effort, high impact quadrant. The sense of accomplishment from checking off these quick wins might give you that much-needed boost to tackle the rest.

Cost value matrix 

In the cost-value matrix, the x-axis represents cost, and the y-axis represents value , leaving you with the following groupings (from top-left going clockwise): 

High value, low cost

High value, high cost 

Low value, high cost

Low value, low cost

Evaluate how much your task will cost (in terms of time) and the value it'll provide. Tasks in your High value, low cost quadrant are your quick wins, while ones in your Low value, high cost are tasks you should probably avoid. 

With any of the matrix models above, you can sketch them out on paper or in a spreadsheet. Or you can use an app that has built-in features for building priority matrices, like TickTick .

2. MoSCoW prioritization method 

The MoSCoW method is a simple prioritization technique where you assign every task on your to-do list to one of four categories:

M – Must do : M tasks are things you absolutely have to do.

S – Should do : S tasks are things you should do, but they're a lower priority than M tasks.

C – Could do : C tasks are nice-to-dos. You'd like to do them, but if you don't, it's probably not a big deal.

W – Won't do : W tasks are things that just aren't worth doing.

Note: If you have a lot of tasks that need delegation, the MoSCow method isn't for you. Jump to the ABCDE method for a better-suited alternative technique. 

After you've assigned each task to a category, delete your W tasks. Breathe a sigh of relief at how much shorter your task list is now. Then get to work. Focus first on M tasks, followed by S tasks, and if you have time, C tasks. 

By working on your list from the top down, you can ensure that you're always working on your highest-priority tasks.

For a seamless way to visualize and organize your tasks, try using a Kanban app like Trello or KanbanFlow . Create a list of unsorted tasks, and then drag each task into the appropriate category. You can also drag and drop tasks up and down within lists to specify the order in which you want to work on them.

But for the MoSCoW method to really work, you have to make sure all your tasks actually get added to your board, so you can categorize them. With Zapier, you can automate this process by connecting your Kanban app with your task sources, like Slack or your email inbox. Learn more about how to automate your Kanban board , or get started with one of these workflows. 

To get started with a Zap template—what we call our pre-made workflows—just click on the button. It only takes a few minutes to set up. You can read more about setting up Zaps here .

Create Trello cards from new saved Slack messages

Create ClickUp tasks for new saved Slack messages

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3. ABCDE method

The ABCDE method comes from Eat That Frog by Brian Tracy. Similar to the MoSCoW prioritization technique, the ABCDE method gets you to assign each task on your list into a category:

A tasks are things you must do.

B tasks are things you should do.

C tasks are nice-to-dos.

D tasks are tasks you should delegate to someone else.

E tasks are tasks you should eliminate.

The ABCDE method expands on the MoSCoW method by helping you prioritize the tasks you need to do and identifying the ones that can be done by someone else. 

Kanban apps also work really well for this technique. You'll have one source list for all your tasks followed by additional lists to contain A , B , C , D , and E tasks. Drag and drop tasks from the source list into the appropriate category, then get started on your A tasks.

4. Scrum prioritization

Scrum prioritization (also referred to as Agile prioritization ) is a prioritization method that relies on ordering your tasks based on both priority and sequence.  This technique works particularly well when you have to take sequence into account.

For example, say your highest priority task is to re-tile your bathroom floor. However, you know that you also need to have plumbers run new pipes in your bathroom, and they'll have to cut into the floor to do so. Getting new pipes run may be a lower priority, but since it will impact your highest-priority task of re-tiling the floor, it needs to be completed first.

In Scrum prioritization, you evaluate each task on your list using three criteria:

How important is this task?

How important is it compared to the other tasks on this list?

Is any other task dependent on this task?

Then, using the answers to those questions, you assign each a unique number from one to n (where n is the total number of tasks on your list). 

Scrum prioritization works well on its own, but it also pairs really well with other techniques like the MoSCoW or ABCDE method. After categorizing your tasks by priority (as M , C , and W or A , B , and C ), you can begin sequencing the tasks in order of how you plan to complete them, keeping in mind any task dependencies that might impact that order.

Any to-do list app that allows for drag-and-drop ordering works well for Scrum prioritization. But if you're organizing tasks with a lot of dependencies, project management apps like Jira and Asana provide a better way to visualize your sequenced tasks. 

5. Bubble sort method

The Bubble sort method is an effective way to answer the question, "How important is this task compared to other tasks on this list?" It's especially useful if you're suffering from the everything-is-urgent problem. 

Start with a horizontal grid and assign each task to a cell. 

Then, take the first two tasks and evaluate them against each other by asking, "Which task is more important?"

Whichever task from the previous step is most important gets moved to the left. In the example below, Task 2 is more important than Task 1 , so the two tasks switch places.

Then compare the next two tasks. Which is more important? The more important task gets moved one cell to the left.

Continue this process until you get to the end of the list. Then repeat the process from the beginning. Continue repeating the exercise until every task is to the left of a less-important task. Your priorities are now listed from left to right.

There's no specific tool designed for the Bubble sort method, but you can easily use a Kanban, to-do list, or project management app for this. The only difference is that your sets of tasks will run from top-to-bottom instead of left-to-right.

6. Most Important Task (MIT) method

Most Important Task (MIT) is an exceptionally simple prioritization method popularized by Leo Babauta . Instead of trying to prioritize your entire to-do list, start every morning by picking one to three MITs—things that you must do that day.

Pick at least one MIT each day that's related to your goals. This way, you're doing something daily to help you reach your goals. And while you'll most likely complete more in a day than only your MITs, selecting your MITs in the morning and setting a deadline ensures you're dedicating time every day to working on important, high-priority tasks.

The nice thing about MIT is that you don't technically even need a to-do list to use the technique. But if you want to keep your MIT top of mind, try the free Chrome extension Momentum . It prompts you to choose a focus for the day the first time you open Chrome, and then it shows you that focus each time you open a new browser tab.

Start every day on the right foot. Here are 12 morning and evening routines you can implement to create more productive days. 

7. The Ivy Lee method

The Ivy Lee Method is simple (and similar to the MIT method):

At the end of every workday, choose the six most important tasks on your list to work on tomorrow.

Then, order those six tasks in terms of priority.

When you get to work the next day, work on task number one until it's complete.

Tackle the rest of your list in the same way.

Continue until all six tasks are complete, and repeat the process every single day.

Any to-do list, Kanban, or project management app works well for the Ivy Lee method. But if you find that you're struggling to complete your prioritized tasks because of meetings and other distractions, consider building your to-do list in a time-blocking app . This way, you can schedule time for that work directly in your calendar, making you unavailable for meetings that probably could've been an email .

8. 1-3-9 prioritization technique

The 1-3-9 prioritization technique encourages you to focus on important tasks, but it also gives you a way to prioritize the less important tasks you'll inevitably need to work on. It's like a blend of the MoSCoW, MIT, and Ivy Lee methods. 

Every day, you plan to complete 13 tasks:

one critical task (like an M task from MoSCoW)

three important tasks (like S from MoSCoW)

nine nice-to-do tasks (like C from MoSCoW)

You could even combine the 1-3-9 technique with an Eisenhower matrix, leaving you with one task in the Important and urgent quadrant, three in the Important but not urgent quadrant, and nine in the Urgent but not important quadrant.

Order your lists of three and nine tasks in terms of priority. Then work on and complete your one task, followed by your three tasks in order, and finally, your nine tasks in order.

In an ideal world, you'd be able to work only on your highest-priority, high-value tasks—but things rarely work out that way. The 1-3-9 method addresses that reality by giving you a way to make sure you're at least working on the most important of your less important tasks.

Any priority matrix app or Kanban app works well with the 1-3-9 prioritization technique. 

9. Two lists technique

Warren Buffett's Two lists technique is another really simple approach to prioritization.

First, you write down a list of 25 things you want to accomplish. When the list is complete, circle the five most important items on that list.

When you're finished, compile the results into two lists:

The first—containing the five tasks you circled—becomes your to-do list.

The second—containing the 20 tasks you didn't circle—becomes your don't-do (yet) list.

Focus your attention on completing all the tasks on your first list. Only then can you begin working on your second list.

The Two Lists technique is designed to be done on paper, but you can accomplish the same thing with any to-do list app that lets you move tasks from one list to another. Or, if you want to get really creative, turn your 25 goals into a doodle .

10. Pareto principle (80/20 rule)

The Pareto principle , also known as the 80/20 rule, states that 80%of consequences tend to come from 20%. For example, 80% of your impact at work comes from 20% of the tasks you do. 

Put another way, if you prioritize a small percentage of the right tasks, they can yield outsized effects. 

To put the 80/20 rule into practice, identify your 20% work and make those tasks your priority. There's no hard-and-fast rule baked into the Pareto principle for how to determine your 20% work, but you can draw on other strategies. For example, M tasks from the MoSCoW method or Low effort, high impact tasks from the effort-impact priority matrix. 

Bonus: How to prioritize tasks for teams

Many of the prioritization methods listed above are useful for prioritizing your daily tasks. But what if you're working on a larger scale? For example, a product team debating which features to roll out next, or a marketing team deciding how to divide their advertising budget across different campaigns. In those cases, here are a few prioritization methods to help your team identify and sequence those tasks: 

Weighted Shortest Job First (WSJF)  

RICE scoring model   

How to pick the right prioritization technique

Every technique on this list helps you achieve the same thing: ensuring that you're always working on your most important tasks. So, in the end, it doesn't matter which technique you use. It doesn't matter if you use multiple techniques. And it doesn't matter if you blend parts of the different techniques to make your own custom method.

What matters is that you pick something that makes sense and feels natural, and get to work.

Related reading:

The upside-down world of bizarro productivity tips

How to manage a busy season when you're struggling with mental health

The best ways to work smarter, not harder

7 mental models to help you make better business decisions

6 to-do list ideas to help you get more done

This article was originally published in July 2019. The most recent update was in September 2023 with contributions from Jessica Lau.

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Jessica Greene

Jessica Greene is a freelance marketing and business writer. A former writing instructor and corporate marketer, she uses her subject-matter expertise and passion for educating others to develop actionable, in-depth, user-focused content.

• Personal productivity
• Project management

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Task Prioritization: Practical Tips And Methods

We’ve all been in a situation where we are being crushed with incoming tasks from one side, while our backlog is slowly piling up on the other side. When that happens, we often decide to get our online to-do lists in a daily task tracker back under control and sort them out. 

That’s when we go through our commitments and find out that all of them are high-priority tasks. And while it can be frustrating and demotivating, it’s not the end of the world.

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Even if every single task appears to be an urgent matter, it rarely is. And in today’s article, we will show you that any to-do list and schedule creator can be dropped down according to priority, and we will share good techniques and recommendations that will make you a task prioritization champion. 

Why is prioritizing tasks an important thing to do?

Many people live their lives without worrying about prioritizing tasks in their daily planners . So why should you? 

When you go with the flow and ignore planning in schedule calendar planner or prioritizing, you often feel that:

• You are constantly playing catch-up
• You risk missing deadlines
• You are failing at your goals
• You are always stressed and overwhelmed
• Your productivity is low
• Your time is wasted

As you see, not paying attention to all your tasks can backfire in many different ways, so it’s best to avoid a catastrophe by prioritizing tasks and working through them before they start dictating your life.

Where to start with prioritizing tasks? Prep work

If you’re not used to working with your to-dos, then you might struggle with finding a starting point. But worry not – we are here to help! 

Step 1: create a single task list

Find all your digital and paper notes, check the digital calendar planner , and look in the mailbox – make sure to go through all places where you sometimes leave tasks for the future self. 

It’s important to find them all and put them in a single master list where you will be able to see your entire workload. We recommend having both personal and work tasks on the list for best results. The two different aspects of our lives are very interconnected and co-dependent, so if you want to maximize the benefits of the exercise, separating the two worlds will be an obstacle.

Pro tip 1: Take a brief look at your master list – are there any to-dos that you already see that can be crossed off? Something that’s not that relevant anymore, or a task that’s associated with something that you are no longer interested in but was afraid to admit to yourself. Maybe some of them can be delegated? Especially if they’ve been pushed back a few times already.

Pro tip 2: Utilize project management tools that will automate and simplify the process for you. Bordio’s free week planner , for example, allows you to have an active to-do list but also a waiting list for tasks without a specific due date or the tasks that can be pushed back without creating any risks or damage. Simple virtual planner by bordio is very handy for such sorting. The waiting list is conveniently placed to the right of your main schedule or to-do list, so you can always add or move items back and forth as you look through your weekly tasks.

Step 2: add information to your tasks

Congratulations! All of your tasks are in one place now, and that’s already a big step towards success. 

Now it is time to do a bit of work with your master list. Go through it in your time management tool for work and see if you can specify:

• The deadline – formal or your preferred one
• Amount of time the task takes to complete
• Level of urgency (for example, not urgent, rather urgent, very urgent)
• Co-dependencies with other tasks on the list

By the way, Bordio’s best task organizer allows you to visualize task priorities with colors, making your prioritized task list even easier to work with.

It’s okay if you won’t have some information for each of the tasks, but the more understanding you gather at this point, the easier it will be for you moving forward. The ultimate goal here is to set you up for success through clarity and organization by providing a single view of your workload and giving you rough estimations of how much time they’ll take and how difficult that would be. 

With that understanding in mind, you are ready to move on to the actual process of prioritizing your tasks. 

Pro tip: If you have tasks with specific due dates and rough estimations of how much time they will take, you can schedule them in your Bordio calendar. Add time blocks to secure the time to work on them.

For example, you need to write a project implementation plan for next Friday that takes 5 hours. Create a task with a deadline and place it in the calendar. Then you can set three separate time blocks (2 hours, 2 hours, and 1 hour) and spread them across a few days to make sure you have the time to finish your plan.

It’s a simple and genius way to avoid situations where you get a notification that the task must be submitted in 2 hours but you’ve completely forgotten about it and haven’t even started yet.

Task prioritization techniques everyone should know

One of the secrets of integrating the process of prioritizing tasks into your life once and for all is to find a method that suits you best and utilize it.

There are many prioritization strategies, methods, and instruments out there that you can use to manage tasks. However, a lot of those techniques are based on the good-old classics which we will share with you now.

By the way, it’s perfectly fine to mix and match, and to move from one technique to another if you feel like it. Whatever helps you stay on track and organized is good.

#1 The 4Ds (do, defer, delegate, delete)

The 4D matrix is more like a time management tool, but it can be a prioritization method as well. Perhaps you’re better off starting with the 4D model and complementing it with some other techniques that we are describing later in the article. 

The matrix tells us to categorize tasks across four categories: Do, Defer, Delegate, and Delete. Once the workload is allocated, you can instantly see important tasks and dedicate your most productive hours to them.

#2 The GTD mind sweep

The GTD mind sweep was created by David Allen and summed up in the legendary Getting Things Done book. We did a separate article on the mind sweep where we dig deeper into the method.

In short, you can use mind sweep for listing all to-dos and prioritizing them later or just for prioritizing tasks. First, you do a brain dump session where you list absolutely everything. It can be an important task or something like throwing out your old socks. Basically, everything that clutters your mind needs to be listed. You then transform all those to-dos and ideas into tasks. The next step is to identify if these tasks are actionable or not, and either go ahead and schedule them right away or push them for the time being.

You can read more about the Getting Things Done hit by Allen and other great books in our best productivity books list.

#3 The Eisenhower matrix

The Eisenhower matrix is extremely popular for prioritizing tasks.

We take all the tasks and organize them into four quadrants based on the tasks’ urgency and importance. Once you do that, it becomes very easy to see what is truly important.

#4 The Eat the Frog method

Eat the frog method was popularized by Brian Tracey, and it is pretty simple when you think about it.

The method tells us to find the most annoying and least exciting thing to do and just go for it. We often have difficult tasks that we don’t even want to think about. So instead, we postpone them again and again, but keep thinking about them, and it ruins our vibe for the entire day.

Tracey says that it’s counterproductive and recommends “eating the frog” first thing in the morning. That will make you fulfilled right away and give you a huge boost for the rest of the day.

By the way, we talk more about Brian Tracey’s and many other great time management books in our dedicated books article , so make sure you check it out for inspiration and tons of great recommendations.

#5 The ABCDE method

With the ABCDE method, you assign letters to your tasks according to their importance and urgency, where A is an absolute must-do, and E is for eliminating.

This method gives us more layers of urgency and importance than the Eisenhower matrix. And it works especially great for people with loads of different tasks, as opposed to someone who deals with similar assignments most of the time.

As and Bs require your immediate attention, and the lower priority level tasks (C, D, E) can be dealt with later or delegated.

#6 The MoSCoW method

This Russian capital-inspired method stands out as it encourages us to prioritize tasks based on their outcomes. It is an excellent project management tool for stakeholders and project managers to see the tasks’ importance for the project’s delivery.

The method divides to-dos in time planner calendar by:

• Must have – if it’s not done, the project is failed.
• Should have – not project-killer if failed but important to do.
• Could have – can be delivered if remaining resources allow.
• Won’t have – an outcome won’t be delivered as a part of the project.

#7 The MIT method

This next method was introduced by Leo Babauta, the author of Zen Habits.

MIT stands for Most Important Tasks.

As Leo recommends, we choose up to three tasks that hold true importance and do them during the day. Once the top-3 are done, and if there is still time left, we move on to other daily tasks. At this point, we select whatever we feel like doing, and it’s okay if they’re not the most critical ones. 

#8 The Pareto principle

The Pareto principle, a.k.a. the 80/20 rule, was introduced by Vilfredo Pareto.

There are two ways we can think about the principle in terms of prioritizing tasks:

• 20% of our tasks generate 80% value, so we need to identify the key 20% and focus on them.
• 80% of our time should be spent on tasks that generate the most outcomes, and 20% can be dedicated to the rest.

#9 Chunking

Chunking is also known as time boxing, and it is a 2-in-1 technique because it helps you prioritize tasks AND it enhances your time management.

With chunking, you are creating focused time blocks where you mustn’t be interrupted for anything else. A time block can be assigned to a complete task (edit the video), part of the bigger task (complete paperwork for an upcoming investment round), or you can group similar tasks together for most efficiency (like a reply to all emails).

By the way, we at Bordio love chunking! One of our favorite tools is time blocking. When creating a task, you can add time blocks and spread them evenly on your schedule to ensure you have sufficient time to do the job. You can’t complete the task unless all time blocks are ticked off.  

Post task prioritization: review, revise, repeat

You are rarely in a situation where nothing changes. It’s almost impossible today not to have a single thing added, removed, or changed from your to-do list once you’ve created it. 

So, to remain productive and up to date, you should go back to your task lists regularly and review them. Should any adjustments be necessary, apply them to the list and see how that affects the priorities you’ve set before. 

As you start prioritizing tasks, don’t forget about other productivity habits. Get started with our productivity tips everyone needs to adopt guide and a list of recommendations to be productive at home , whether you work or rest. It is also helpful to learn to manage your time more effectively, so if you’re up for it – have a look at our critical time management skills piece.

More tips for better task prioritization

Here are some more recommendations to help you prioritize tasks like an absolute pro.

#1 Write everything down

Any task – big or small – should be written down. It’s great if you have a good memory but nobody can remember everything all the time. Plus why would you torture your brain like that?

Once written down, you won’t forget about it but you will also release yourself from having to keep it in the back of your mind at all times.

Did you know that Bordio has a mobile app, so you can always add tasks to your list wherever you are? Standing in line in the pub, walking your dog in the park, or watching your favorite TV show.

#2 Avoid priority competition

It’s okay if you have a few easy tasks that you can work on simultaneously.

But as we are trying to focus on top priority critical tasks first, these are the types of to-dos that are best handled when worked on exclusively. Multitasking leads to worse results across all the work you are trying to do at once. Staying focused on one priority at a time, in turn, will reduce the time it takes you to complete the job, and you make fewer errors.

#3 Break down large goals

Always break down large goals into smaller bits that are easier to manage. Smaller tasks seem more realistic to tackle in one sitting. And one of the key reasons we procrastinate and postpone doing something is because the job just seems too grand.

#4 Think and plan long-term

Long-term planning allows us to consider upcoming goals and tasks when we plan for the next week or month.

If you know about future complex tasks in advance, you can incorporate them into your schedule better. That means they are more likely to be achieved with the best results and within deadlines. For example, you may have a slow period where your monthly tasks are not too demanding, so you choose to relax a little. Yet, if you know that in about two months several important projects will be launched, you might consider doing some extra work now when you have free time and a laid-back schedule.

#5 Differentiate between urgent and important

We must remember that urgent and important tasks are not the same.

They both seek our attention and pop up at the top of our priority list. As you are looking to find time for both, double-check that you are not only dealing with urgent but unimportant tasks.

For example, you can dedicate 2 hours each day to urgent tasks, and then the rest should be allocated for important work. The less pressing jobs should either be handled in the leftover time, delegated, or eliminated.

#6 Be realistic

It’s unlikely that you will be able to complete all the tasks on your to-do list during the time that you have mentally allocated for it. Don’t set yourself up for failure. Be realistic and flexible.

Some people say that you should not have more than six tasks assigned per day, and only 2-3 of those tasks can be urgent and important. Listen to your inner voice when it comes to creating your own task list, but remember that you are not winning anything by being the busiest or the most overwhelmed.

Final thoughts on task prioritization

Prioritized work translates to productive work. 

Planning and organizing your to-do list will save lots of time and stress in the future. You will be better prepared and less likely to procrastinate. Having structure will avoid workload fatigue where you are too overwhelmed with what must be done even to try and tackle it. In addition, we also recommend trying the handy electronic planner app and task maker app .

Don’t wait for when the situation becomes so critical that you have no other choice but to work through the task list and prioritize your work. Start today and enjoy meeting deadlines, better time and resource use, and an optimized workload for you and your team.

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How to Prioritize Tasks in 6 Easy Steps

Contents page

The Importance of Prioritizing Tasks

Understanding task prioritization, how to prioritize tasks, how to prioritize tasks: techniques and strategies, how to prioritize tasks at work: tips for success, prioritize your tasks in timecamp planner, how to prioritize tasks: overcoming common challenges, mastering task prioritization for greater productivity.

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In today's fast-paced work environment, effectively managing your workload and prioritizing tasks is essential for productivity and success. Knowing how to prioritize tasks can help you stay organized, reduce stress, and increase efficiency.

There are only so many hours in a day and it's impossible to do everything at once. But when you know how to manage your to-do list, you can easily hit deadlines with ease.

In this article, we'll explore techniques and strategies for task prioritization, helping you take control of your workload and achieve more.

Prioritizing tasks is crucial in efficient time management. It allows you to manage your daily workload effectively . By identifying which tasks are most important, you can allocate appropriate time and resources to each task to prevent overload.

That way you can stay focused on activities that are most important and impactful, and eliminate competing priorities. Thereby, you can avoid last-minute rushes and smoothly meet deadlines.

Setting your priorities straight is also important because it helps in smart decision-making . You can better assess your capabilities and allocate efforts and resources properly.

Prioritizing tasks enables you to adapt to changing circumstances effectively . As new tasks or challenges arise, you can reassess priorities and make informed decisions about how to adjust your plans and resources accordingly.

While effective scheduling helps individuals keep focus, it's also important for teams. Too many tasks in a project make it difficult to achieve good outcomes. With prioritized work, all team members can focus on their duties and reduce stress.

Task prioritization is the process of arranging tasks in order of importance or urgency, ensuring that you focus on the most critical tasks first. By learning how to prioritize work, you can ensure that your time and energy are allocated effectively, resulting in greater productivity and reduced stress.

When you know what needs to be done and when you can focus your energy and attention on completing tasks rather than worrying about what might have been missed.

When Should You Prioritize?

Prioritization is a valuable skill that can help you minimize workload. But not all tasks should be taken into account when you plan your schedule. Sometimes you may be assigned just a task that doesn't bring much value to your work. And you can either do it quickly in a few minutes or postpone it.

But there are important tasks that have a high priority level and need your attention. So how to know when to prioritize your tasks at work? Here are the main determinants:

You have too much work and are tired of work overload.

Are stressed, have work or personal problems, and need to work on your work-life balance.

Are playing catch up all the time and are always past the due date.

Have a new project or a new job and need to decide on daily priorities and the importance of work tasks so you can work effectively.

When it's hard to reach personal or professional goals.

You want to improve personal or team productivity.

Which Tasks Should Be Top Priority?

It can be difficult to choose the right tasks for prioritization. Especially if you have too much work and everything seems to be important. To get away from the entanglement of tasks, you can easily define important work by sorting urgent and important from unimportant tasks.

Here are examples of different responsibilities that often require top priority:

Urgent and time-sensitive tasks —tasks that have imminent deadlines or are time-critical. These could include delivering time-sensitive projects, responding to urgent emails or requests, or meeting important client or stakeholder demands.

High-impact and high-value tasks —work that has a significant impact on your goals, projects, or organization. These tasks contribute directly to achieving key objectives, driving revenue, or creating value. They might include important strategic initiatives, critical client deliverables, or tasks that generate significant long-term benefits.

Dependencies and bottlenecks —these are crucial for unblocking other activities or projects. Identifying assignments that act as dependencies or bottlenecks for other tasks or team members helps ensure smooth workflow and progress.

Duties with potential risks or consequences —some activities carry higher risks or potential negative consequences if not addressed promptly (e.g., compliance-related tasks, resolving critical issues or conflicts, or actions related to legal or financial matters). Prioritizing these helps mitigate risks, avoid costly mistakes, or prevent further complications.

Tasks that align with long-term goals —activities that contribute to your long-term objectives or personal development (e.g., learning new technologies, investing time in professional networking, or pursuing personal development opportunities). These might not have immediate deadlines or be urgent, but they are important for professional growth, skill-building, or achieving significant milestones.

In general, your first concern should always be a very important task that improves your workflow, increases efficiency, or boosts productivity. It will have long-term benefits. These could include automating repetitive tasks, streamlining processes, adopting new tools or technologies, or conducting performance reviews to identify areas for improvement.

Before you start prioritizing, sit down with a cup of your favorite drink: it's important that you don't feel stressed when defining your priorities. With the right approach, you can make it easy to tackle your to-do list, and avoid stress and bad emotions.

Here's what you need to do to prioritize your work step-by-step:

1. Create a Task List

Begin by listing all the tasks you need to complete, making sure to include both short-term and long-term tasks.

What to include in your task list? It's a good idea to make a master list and outline all activities that you perform on a daily basis: small tasks and bigger tasks. These may include all types of your daily tasks (phone calls, meetings, regular duties, answering emails, etc.)

Remember not to include everything. In general, the more tasks you include to consider, the better. But adding every tiny detail that isn't the actual work (such as making coffee or taking a break) will bring more confusion. Stick to the project plan for clarity.

2. Assess the Importance and Urgency

Evaluate each task based on its importance and urgency, using criteria such as deadlines, potential impact, and stakeholder expectations.

The general rule says to distinguish between urgent and important tasks. Urgent tasks are those that you need to address immediately. While important tasks are those that bring you and your team closer to reaching goals but you can take your time to perform them. Also, important tasks can also be urgent, so you need to assess properly to avoid confusion.

Difficult tasks should also be given the right attribute.

4. Allocate Time and Resources

Once you have identified your high-priority tasks, allocate your time and resources accordingly, ensuring that you can effectively complete these tasks before moving on to lower-priority items.

It's easier to prioritize tasks based on their time relevance—daily tasks, weekly tasks, monthly tasks—so you can decide which of them need most of your time, which can be performed in a team and individually.

5. Schedule Your Day

Plan your day around your prioritized task list, ensuring that you allocate sufficient time for each task and avoid distractions.

Use prioritization methods such as task batching, time boxing, or time blocking to set specific amounts of time on each task and effectively work till the end of the day without distractions.

6. Review and Adjust

Task prioritization is a dynamic process, and priorities may change based on shifting circumstances, new information, or emerging priorities.

Tasks can change depending on your availability, workload, personal and work situation, and other factors. Regularly reassessing and adjusting priorities ensures that you concentrate on what matters most and adapt to evolving needs effectively.

It's important that you review your task list so you have a complete picture of your workload and continue pushing.

There are many techniques recommended by productivity experts that can help you better achieve your goals. Here are a few of the most popular and proven approaches:

Time tracking : it's one of the most popular methods to effectively prioritize your tasks. By automatically tracking your activities (with apps such as TimeCamp), you can spot distractions, maintain attention, and save time and money.

Eat the frog: ushered in by Mark Twain, this method was popularized by Brian Tracy—do the most important task first.

Use a prioritization matrix : a prioritization matrix, such as the Eisenhower Matrix or the ABCDE method, can help you visually organize tasks based on their importance and urgency. This can make it easier to determine which tasks should be prioritized.

SMART goals: they are designed to be S pecific and well-defined, M easurable to track progress, A chievable within realistic parameters, R elevant to the broader objectives, and T ime-bound with a specific deadline for completion.

Story points : used in Agile project management methodologies, story points help to assess the complexity and difficulty of a project.

Pomodoro technique: a time management method that involves breaking work into focused intervals, typically 25 minutes long, separated by short breaks, to enhance productivity and concentration.

For more prioritization strategies, check out the guide on 15+ effective time management methods .

Except for using the right techniques and properly forming your schedule, you can make additional small steps to improve your results. Here's what you can do:

1. Break down large tasks: large, complex tasks can be overwhelming, making it difficult to prioritize effectively. Break down these tasks into smaller, manageable steps, and prioritize each step individually.

2. Reassess your priorities regularly: as your workload and circumstances change, your priorities may shift. Regularly reassess your task list and adjust your priorities as needed.

3. Stay flexible: while it's essential to have a clear plan and prioritized task list, it's also crucial to remain flexible and adapt to changing circumstances. Be prepared to adjust your priorities when necessary.

4. Delegate when possible: delegating tasks to others can help you focus on your highest-priority tasks, improving your overall productivity.

5. Avoid multitasking: multitasking can reduce your focus and productivity. Instead, concentrate on one task at a time, ensuring that you can give each item from your list the attention it deserves.

6. Have a daily routine : daily habits are a powerful tool. Create your typical day with a daily routine that helps you overcome challenges and makes it easier to keep things under control.

TimeCamp Planner is a project management tool that allows you to schedule workflow, communicate and collaborate with teams on company projects.

It can help you prioritize tasks with prioritization steps in an easy and intuitive way with a user-friendly interface and helpful features. This is how you can quickly organize your business:

1) Define your workload: create a task list with just one click. TimeCamp Planner allows you to quickly add a priority list with important action steps. You can also make your to-do list more granular and visualize work with different views. That way, you can choose to work with your favorite prioritization method.

2) Assess the importance and allocate time and resources : once you decide on priority, you can create workspaces and add job titles, assign people to specific duties, and make a checklist with assignees.

3) Schedule work: add due dates and set estimates. They will help you stay on track and complete work on time. You can always adjust them in real time.

4) Monitor progress: you can view the progress with a timeline view. Real-time dashboard view and notifications allow you to stay on track through the whole project lifecycle. Group and individual chats let your team stay connected and collaborate better on a common goal.

Additional features allow you to become even more productive and help your team reach every goal.

When trying to prioritize your work, there are three main challenges that can significantly impact your productivity. Take a look at what they are and what you can do with them:

1. Procrastination

To overcome procrastination, break tasks into smaller steps, set clear deadlines, and create a structured schedule to keep yourself accountable.

2. Overwhelm

Feeling overwhelmed by your task list can make it difficult to prioritize. Practice mindfulness techniques, such as deep breathing or meditation, to help calm your mind and focus on the task at hand.

3. Perfectionism

Perfectionism can hinder your ability to prioritize tasks effectively. Learn to accept that not every task can be completed perfectly and focus on achieving progress rather than perfection.

Learning how to prioritize tasks is an essential skill for managing your workload and staying productive in today's fast-paced work environment. By using prioritization techniques, regularly reassessing your priorities, and overcoming common challenges, you can take control of your workload and achieve more.

Prioritizing tasks is important because you ensure that there are no missed deadlines, you deliver successful projects, and have a high work-life balance. With effective task prioritization, you'll be better equipped to tackle your tasks with confidence and efficiency.

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How to prioritize tasks when everything’s important

“busyness” doesn’t always equate with progress—learning how to prioritize tasks will help you make the most of your workday.

During the workday, tasks are often prioritized (or not) according to the needs of others or the immediacy of deadlines. This can happen in our personal lives, too, with limited time spent on activities that are actually important , and more energy spent being “busy.” Prioritizing tasks effectively—with intention and according to future goals —can change this, ensuring that every task you tackle drives value and keeping unimportant tasks from cluttering your to-do list. 

By implementing prioritization strategies, you can drastically change the arc of your workday to really make the most of your time in the office —and at home. Whether you’re a sole proprietor or an executive at a Fortune 500 company, these strategies will help you evaluate and set your top priorities. 

Seven strategies for prioritizing tasks at work   

Thoughtful prioritization typically involves creating an agenda, evaluating tasks, and allocating time and work to bring the most value in a short amount of time. Prioritization should be flexible, as you may need to interrupt low-priority tasks for urgent must-dos. 

1. Have a list that contains all tasks in one 

Effective prioritization comes from understanding the full scope of what you need to get done—even the most mundane tasks should be written down and considered. To give yourself a complete picture, it’s a good idea to include both personal and workday tasks in a single task list. 

Everything from picking up your dry cleaning to scheduling a one-on-one meeting with your boss should be captured in the same place. Once everything is written down, prioritization typically happens according to the importance, urgency, length, and reward of each task.

2. Identify what’s important: Understanding your true goals 

While it might seem like an immediate time management strategy , prioritization is key in achieving long-term goals. Understanding what you’re really working toward—be it a promotion, a finished project, or a career change—helps you identify the tasks most pertinent to those future outcomes. It can be a good idea to break these larger goals into smaller, time-related goals. For example, a yearly goal can be deconstructed into monthly to-do lists, which then lead to weekly tasks, daily priorities, etc. 

For Alejandro Cerecedo, a senior fashion account executive at PR firm  Another Company  and a member at  WeWork Reforma 26  in Mexico City, setting long-term goals is how he aligns and motivates his team at the beginning of each year. “We talk about their personal and professional goals, and we set a timeline for how we’re going to achieve them,” Cerecedo says.

This big-picture thinking is vital in prioritizing effectively: It’s a common misconception that being busy equates with progress. However, filling your day with tasks that have no impact on an ultimate goal is time wasted . Be honest with yourself about the lasting value of each task, and always have the end-goal in mind.  

3. Highlight what’s urgent

Your to-do list should provide full visibility of deadlines, helping you to identify which tasks must be completed promptly and to plan ahead according to future deadlines. 

Creating deadlines even when they’re not formally required is also important; otherwise, you will continue pushing back important tasks simply because they aren’t time-sensitive. (This strategy can also be helpful in increasing productivity and reducing procrastination.)

4. Prioritize tasks based on importance and urgency 

In his 1989 book The 7 Habits of Highly Effective People , businessman and keynote speaker Stephen Covey suggests tasks should be categorized (and then prioritized) according to importance and urgency.   

• Urgent and important: These tasks should be done first
• Important but not urgent: Block off time on your calendar to get this done, without interruption
• Urgent but unimportant: Delegate. Delegate. Delegate. 
• Neither urgent or important: Remove from your to-do list

Another strategy for ensuring important tasks are prioritized —even above asks from pushy stakeholders or “urgent” ad-hoc requests—is the Most Important Tasks (MIT) methodology . This strategy involves creating a separate list of just three tasks that must be done that day. These tasks should be chosen more for their importance than their urgency. To decide, ask yourself goal-oriented questions: What tasks will have the biggest impact on the end result? What can I get done today to further my progress toward that goal? 

5. Avoid competing priorities 

When the tasks you’re working on aren’t particularly difficult, it’s relatively easy to manage them in tandem. However, as difficulty increases, research shows people who are in positions of power are more likely to prioritize a single goal, whereas people in low-powered positions will continue to try and manage multiple priorities. This dual-task strategy has been linked to a decline in performance, which means the most important tasks aren’t fulfilled to the highest standard. 

A tactic for staying focused on one important task at a time is identifying likely distractions—concurrent tasks or ad-hoc requests—and actively avoiding them throughout the day. This means if you’re tasked with pulling data for a project at the same time you’re creating slides for a presentation, you should prioritize one task and avoid any work, emails, messages, or preparation related to the other. 

6. Consider effort

When staring at a long to-do list, it’s easy to become overwhelmed by the work that needs doing—a feeling that reduces productivity and leads to procrastination. A strategy to overcome this involves evaluating tasks according to the effort required to complete them. 

If your to-do list is becoming too burdensome, prioritize those tasks that require minimal time and effort and move through them quickly. This clearing of tasks will give you some breathing space and generate a sense of accomplishment to propel you throughout the day. 

7. Review constantly and be realistic

One of the steps in the five-step “Get Things Done” (GTD) methodology from productivity consultant David Allen involves critical reflection. Frequently reviewing your task list and priorities is key in “regaining control and focus”, Allen argues .

Quick tips for effective task prioritization

As you realize the necessity of proper prioritization, it can suddenly feel more complicated—and more stress-inducing—than creating a simple task list. The key strategies mentioned above are summarized below, to help you set your priorities with intention. 

• Write everything down: Personal and work tasks should be captured in one place.
• Evaluate long-term goals: Consider your larger long-term goals, and the work you need to do to reach them. 
• Break down larger goals: To understand how to achieve your long-term goals, break them down into yearly, monthly, and weekly achievements. 
• Create clear deadlines: Give yourself full visibility of deadlines, and create deadlines for yourself when none are formally required. 
• Employ the urgent-versus-important method: Prioritize urgent and important tasks; set a specific time to work on important nonurgent tasks; and delegate or remove all other tasks. 
• Create a daily MIT list: Write down three important tasks that should be done that day. These tasks should always relate to your larger, future goals. TEST
• Avoid distractions: Intentionally steer clear of competing tasks, especially as task difficulty increases. 
• Consider effort: When your task list is becoming too much, prioritize according to effort and breeze through those easier tasks more quickly.

Prioritize your time and be realistic 

No matter how well you prioritize, there is only so much you can achieve in one day, and certain distractions are impossible to avoid. It’s important to be realistic in setting goals and prioritizing tasks. Otherwise, you’ll create false expectations of those around you, and you’ll constantly feel as if you’re falling behind.

Remember, the purpose of prioritization is to spend time working on the important tasks, those things that will make a difference in the long run and move you in the right direction. When prioritization is handled well, you’ll feel less reactive and more focused and intentional. The aim is to complete work that signifies true progress, and let all the rest—all the “busyness”—fall to the wayside. 

This article was originally published on February 6, 2020, and has been updated throughout by the editors.

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Caitlin Bishop was a writer for WeWork’s  Ideas by We , based in New York City. Previously, she was a journalist and editor at  Mamamia  in Sydney, Australia, and a contributing reporter at  Gotham Gazette .

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How to prioritize tasks the right way

“It’s 10 AM, do you know what your team is working on?”

Prioritizing tasks is the lifeblood of a thriving project. Without task prioritization, your team is either frozen in place or aimlessly checking boxes and completing tasks that may or may not add up to something shippable.

Learning how to prioritize tasks is the kind of lesson that benefits everyone. It empowers your team to stay on track free of micromanagement, and as the project manager, you get to deliver projects in scope, within budget, and on time. What more could you ask for? In this article, we’ll share the core framework project managers use to streamline task management.

Why prioritizing project tasks is critical

Projects often have hundreds or even thousands of tasks that require strict scheduling based on client due dates, available resources, and employee capacity. Creating an efficient machine like that takes discipline, clear procedures, and oversight.

Unfortunately, we’re all subject to what Daniel Kahneman refers to as the planning fallacy .  Which means we have a tendency to underestimate the time and resources necessary to complete a future task, due to a reliance on overly optimistic performance scenarios.

Put plainly, we fall into the trap of thinking the future isn’t as full of obstacles as it is right now and tend to overextend ourselves as a result.

Here’s where task prioritization is so important. Without it, our deliverables ship late, and our long-term goals stay goals — not realities.

Instead of stretching ourselves thin or settling for poor performance, we can prioritize the essential workload and see a monumental difference in returns. Here’s how:

8 steps for prioritizing project tasks

The first step to learning how to prioritize tasks is by taking a step back from your task list.

Take a step further than even the project and head up to a 30,000-foot view so you can take stock of the big picture and put things into perspective. Realign yourself with the overall strategy, top priority, and long term goals you’re hoping to achieve.

With a fresh perspective, make your descent lower into the project details and dive in:

1. Develop a Work Breakdown Structure (WBS)

Nothing helps you prioritize your task list quite like creating a master list of all your tasks and breaking them down into smaller chunks that individuals and small teams can easily handle.

(Image Source)

An excellent WBS is one that is mutually exclusive and completely exhaustive, which means there’s no task overlap. Without overlap, every single task is distinct from all other tasks, thus reducing unnecessary ambiguity or double-work.

There are two types of WBS:

• Functional WBS:  centers around the business’s functional aspects, such as project management, software engineering, and system engineering.
• Deliverable WBS:  focuses on the different deliverables a project creates and provides a more cohesive setup.

The deliverable WBS provides the greatest control and traceability, meaning you can follow a critical path for deliverables from beginning to end and increased collaboration since no department is working in a silo.

2. Designate work packages

Work packages are the lowest level of detail in a WBS, and they’re often structured in a “verb-noun” fashion to illustrate the action required to accomplish a specific item.

A work package might be to “develop requirement traceability matrix” or to “program sort routine.” That specificity is key to understanding what’s possible considering the constraints of cost, time, quality, and scope. You can use a planning resource like monday.com templates to map this out easily.

3. Define the necessary activities

Activities are often referred to as “tasks” in modern project management software . Tasks are another notch below work packages and are essentially the steps taken by individuals or groups that are measurable.

Measurability is an essential component here since project managers need to determine their current state subjectively. Some measures work better than others.

For instance, gauging development completion by percentage (E.g., 25% complete) is often imperfect since it’s hard to objectively measure 25% or 50% of any given task. A better gauge is a specific accomplishment (E.g., develop outline) since you can tell whether it’s complete or not.

4. Decide on a logic framework

Logic is what really moves the needle on prioritizing tasks. Logic dictates what order you do your tasks based on common sense. The three types of logic are hard, soft, and false. It’s best to use hard logic, which means there are valid links between activities. A great analogy here is laying the foundation for a house is essential before you can put walls up.

Soft logic references start/stop dates based on an activity’s perceived requirements. Following the same analogy scheme, rough electrical can’t start until rough plumbing is complete. While that’s true for most construction projects , it’s not technically a hard-and-fast rule.

Finally, there’s false logic, which means there are false preconceived notions of when an activity can start or stop.

An example here might be telling someone you can’t do landscaping work until the driveway concrete cures completely. That logic is simply not true. You can absolutely do both simultaneously, hence it’s false logic.

It’s always good to run your timeline by subject matter experts to ensure all dependencies in your schedule are true dependencies and not based on false logic.

5. Assign work effort and resources

There are few things as valuable as estimates that accurately reflect reality. Defining resources in a structured way is challenging but doable. Start by defining a few resource types, such as:

• Project experience and technical expertise
• Hardware such as computers or tangible resources
• Software or intangible assets
• Materials and subcontractors

Then jump into defining the amount of work for each resource. Not the exact quantity but the amount.

For example, a task could require 40 hours of work by a technical expert, 30 tons of material, or the reservation of a testing center. Having system in place to assign one or multiple people — like the People Column on monday.com — can save you much-needed time.

6. Designate a timeframe

How long will your tasks take, and when do they need to begin? If you know a task requires 40 hours of work but must be done in 2 days, you’ll need to tackle it differently (I.e., with more people) than if it can be completed over 2 weeks.

By figuring out when you need material and how long lead times are, you can determine when orders have to go out, and whether you’ll have to pay for rush shipping or not.

The primary focus here is creating a plan of attack for some immediate milestones in the schedule that aren’t necessarily hard deadlines or requirements but preferred expectations to keep the project on pace.

It’s also a good idea to overestimate how long some tasks will take, especially if there are a lot of unknowns. For instance, if you think a task will take 3 hours to complete but you’ve never done it before, you may want to schedule 4. That 25% buffer can make a world of difference when priorities inevitably shift mid-project.

7. Analyze the schedule

Does your plan and schedule make sense? A quick review should give you a rough idea of whether it sounds doable or not. It’s also important to consider whether you’ll meet your contractual obligations with it.

Make task prioritization effortless with monday.com

Learning how to prioritize the right way  may seem daunting, but not if you use the right tool for the job. monday.com is a platform that strikes a rare balance between being feature-rich and still simple to use.

We also make it incredibly easy to get started with the 200+ templates you can copy, so you’re not building workflows from scratch. Try creating a custom WBS with the Work Breakdown Schedule Template  (shown below).

Nothing gets to the heart of learning how to prioritize tasks like an in-depth board that will visually show you all moving project pieces at once. Status, timeline, expenses, resources, importance, effort tracking, it’s all there!

The WBS template will help you prioritize what’s urgent, and when you’re all set, you can create task tracking views so you can get a granular view when you’re ready.

The WBS and task prioritization features of monday.com are just the tip of the iceberg. Here are some other game-changing features you’ll enjoy:

• Stay on top of your priority list with custom automations and notifications
• Tap into your existing workflow and tools with 40+ integrations
• Stay mobile with iOS and Android apps
• Collaborate and communicate about work tasks with real-time messaging and tagging
• Stay on top of your entire portfolio with 8 different data visualizations
• Use resource management for workload reports to see who’s underutilized and who’s overworked

Task prioritization made simple

As you can see, task prioritization matters, and so does having the right task management software .

Putting a project in motion without proper resources, inputs, materials, and task assignments is a recipe for disaster. One that will likely create bottlenecks, duplicate efforts, and an inefficient delivery schedule.

Alternatively, when your task schedule is air-tight and flexible, great things happen. Productivity soars, stakeholders applaud, and you no longer feel a false sense of urgency. Instead, you get clarity and work-life balance knowing everything’s under control.

Chat with our sales team to see how monday.com will help your team prioritize or sign up for a free trial. 

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Project management

7 steps for prioritizing your workload

"Our life is the sum total of all the decisions we make every day, and those decisions are determined by our priorities." ~ Myles Munroe , Author and Leadership Consultant

Do you find yourself juggling multiple tasks and projects, feeling swamped, and not knowing where to start? 

Tight deadlines, looming tasks, and heavy workloads can be intimidating. They leave you overwhelmed and stuck, unable to figure out where to begin and ultimately derailing your productivity.

But fret not! 

Prioritization is a powerful skill that will help you take control of your workflow and optimize productivity . Breaking your workload into manageable chunks and setting priorities helps you break the cycle of missed deadlines, last-minute rushes, and procrastination. 

And task prioritization is simple — rethink how you approach your work and establish a defined process that works for you. Below are seven steps to help you figure out how to prioritize important tasks and maximize productivity.

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Map your workflow for simpler reporting and a better understanding of how work is progressing with this free project tracker template.

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1. Write down all of your tasks

First things first: you need to get all your to-dos out of your head and onto the page — or, in most cases, the screen.

Start by compiling a literal master list of action items that you need to take care of, sorting them based on the following:

Daily tasks: Day-to-day duties, such as meetings, check-ins, and responding to communication.

Weekly tasks: Action items related to client deadlines, presentations, or project milestones .

Month-plus tasks: Tasks related to longer-term projects and goals.

Listing your tasks as individual items immediately makes your workload feel less overwhelming. So much of how to prioritize tasks revolves around knowing what’s actually on your plate. 

This is where a tool like Teamwork.com really comes in handy.

For example, our platform makes recording your personal daily tasks a cinch. Don't rack your brain — just mark off your checklist as needed or tag tasks with different priority levels.

What makes Personal Tasks especially handy is that your project tasks are visible to the entire team, right beneath your fully private personal tasks. Ideally, your team-wide tasks should be public, not only for the sake of the organization, but also for accountability.

By using a board view (Kanban board) for your tasks, you can document your schedule and have a clear understanding of deadlines.

2. Determine which tasks are truly important

Not all tasks are equally important. However, it’s human nature to complete the small, easier tasks first, regardless of importance. But did you know that research shows that tackling these easy wins first can actually give us a false sense of progress ?

You must fight this completion bias and be conscious of the importance attributed to each task. 

How do you achieve this? By distinguishing urgent tasks from important tasks.

Urgent tasks are time sensitive and require immediate attention now, in the next few hours, or by the end of today. You can’t afford to overlook them and risk the consequences of missing deadlines.

Important tasks are not always urgent but must be on your to-do list. You don’t have to execute or complete them today, but you can’t forget them.

Ask yourself these questions to help categorize your tasks:

Who does this impact?: For example, is a task only going to impact you? Your clients? Your department or company? 

What is the reward or outcome of completing this task?: A new client contract? More revenue? A project getting done ahead of schedule?

What is the risk of not completing the task? : Lost clients or revenue? Lost progress on a project?

Once you’ve determined each task’s priority level, there are few different approaches you can use to organize and manage them.

Eat the frog: Not as literal as it sounds. This means tackling the most challenging and time-consuming task first. In project management, this task would most likely be listed as “high priority.” You can then slot the remaining tasks based on their deadline, impact, or reward.

Eisenhower matrix: Also called Urgent-Important Matrix, this visualization project management tool classifies each task into four quadrants based on importance and urgency — important and urgent (do now), important but not urgent (schedule), not important but urgent (delegate), and not important and not urgent (eliminate).

ABCDE Method: With this method, you assign alphabetical priority levels to your tasks: A (top priority), B (high priority), C (medium priority), D (low priority), and E (no priority).

Whichever method you choose, proper time management is key to staying productive and organized during your workday.

3. Stick to a schedule that reflects your priority tasks

You probably have a calendar with all of your events and appointments. But what about the tasks you need to complete? Are you planning them according to their priority? Take a moment and look at what you planned for tomorrow. Does it reflect the priority of the work you need to get done?

If not, it’s time to make a schedule reflecting your priority tasks.

Creating and maintaining a schedule is the ultimate way to stay on track and improve work performance . You can anticipate hectic days, block off time for focused work, and set daily reminders to stay on top of your tasks.

Schedule your most important tasks in advance, based on their priority and due dates. Before your workday ends, create a plan for the next day and allocate enough time to focus on each task.

But don’t forget to leave some time for flexibility in your schedule. You can’t anticipate every surprise, so be sure to build in extra breathing room. Have some “me time” to de-stress, relax and go for a walk, or get some fresh air.

You get things done faster when you know exactly what needs to be done and by when, and this is where the right tools come in handy. Teamwork.com makes scheduling and task management a breeze. You can set up reminders and automate tasks so you don’t miss deadlines, while the board and calendar views help you to visualize your tasks and prioritize them according to their level of importance.

4. Tackle your most intense, high-effort tasks first

Highest priority tasks, especially those with tight deadlines, often require more effort, energy, and attention.

Remember “eating the frog”?

Rather than starting your day by checking emails or social media, take a deep dive into your most demanding task first. Tackle it with full focus and energy before moving on to the next one. This way, you can complete the most challenging tasks before your energy level tanks.

Once you start the small tasks, your dopamine levels will rise, and you’ll be tempted to keep doing them. It might be more rewarding in the short term but won’t be beneficial in the long run.

Teamwork.com’s resource allocation features provide a bird’s-eye view of your team’s tasks. You can see what’s keeping your team busy, when they need help, and how far along a task has progressed. You can delete or add tasks, adjust their deadlines, and assign resources accordingly.

5. Focus on one task at a time (hint: try not to multitask)

"Focusing on one thing without interruption is how you get meaningful work done." ~ Nate Green , Author and Marketing Strategist

Multitasking might seem like a smart move, right?

But trying to tick too many tasks off your schedule at once is a recipe for burnout.

As noted by the Cleveland Clinic, only about 2.5% of people can multitask effectively. For the rest of us, juggling a bunch of different responsibilities and tasks everyday results in poor outcomes. 

And this makes perfect sense in a workplace setting. If you’re constantly bouncing between apps, documents, and email, chances are you’re getting very little actual work done.

We acknowledge that everyone’s different in terms of productivity. For the sake of staying focused at work , though, we recommend tackling one single task at a time.

Alternatively, you can use strategies like time chunking or the Pomodoro technique to break up those tedious tasks that really take a toll on your brain.

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Get a complete picture of everyone's capacity at-a-glance, or forecast and plan resources for future projects with Teamwork.com.

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6. Acknowledge what you realistically can and can’t get done

This is a straightforward tip but something definitely worth mentioning.

There are only so many hours in the day. If you find yourself stressing out over a menial task or something’s creating a massive mental block, be prepared to either drop it or put it off for tomorrow when you’re fresh.

We’re not saying to neglect your work, but rather, be realistic. Companies and teams today are increasingly sensitive to burnout , and rightfully so. 

Granted you’re not impacting the rest of your team’s work or losing clients or direct revenue, give yourself some flexibility.

7. If possible, make prioritizing tasks a team-wide effort

Remember: Your work doesn’t happen in a vacuum.

Figuring out how to prioritize tasks doesn’t have to be a solo act. For example, teams today should enable workers to:

Communicate issues or questions over deadlines and assigned tasks

Collaborate and agree upon on deadlines, scope of work, and roles

Get deep work done and focus on tasks uninterrupted

This speaks to the value of having your company communicate within a tool like Teamwork.com. Between our chat app and board review, team members can manage their schedules, stay in touch regarding priority tasks, and ask questions and collaborate as needed.

• Prioritize your tasks easily with Teamwork.com

Trying to juggle all the tasks on your plate can feel like a never-ending cycle of chaos. Your work-life balance takes a hit as you try to meet all the looming deadlines.

But it doesn’t have to be that way.

Teamwork.com is designed to help you and your teams get tasks done on time, stay in sync, and easily manage your workload. With our comprehensive suite of tools, you can create and update tasks, assign work to team members, review progress in real time, and set up notifications so nothing slips through the cracks. 

Sign up for a free trial today and see how Teamwork.com can help you prioritize tasks and maximize your team’s productivity.

• Task prioritization FAQs

How do you prioritize tasks effectively?

Start by creating a comprehensive task list and identifying deadlines. Assess the importance, urgency, effort, and resources required for each one. Then prioritize your list based on the most urgent tasks, considering dependencies and breaking down complex tasks if necessary. Finally, adapt and reassess priorities regularly, and use project management tools to organize and track progress.

How do you plan and prioritize your workload?

You need to understand your goals and break them down into smaller tasks. From there, you can determine the importance and urgency of tasks, focusing on those that align with your goals and have imminent deadlines. You should also regularly review and adjust your priorities as needed.

How do I create a priority list?

Consider the urgency, effort, and resources required to complete all the tasks on your to-do list. Then order the list based on priority, focusing on high-value and urgent tasks first. You should also regularly revisit and update the list to adapt to changing circumstances.

What is the most important step in prioritizing?

While all the steps in prioritizing tasks are crucial, the most critical step is understanding how each task ranks in relation to your goals and objectives. By assessing the significance of tasks, you can prioritize effectively and ensure your efforts are directed toward activities that align with your overarching priorities.

What is the role of project management software to manage your work priorities?

Project management software plays a crucial role in managing work priorities by providing a centralized platform for task organization, priority setting, deadline management, collaboration, resource allocation, progress tracking, and adaptability. It helps individuals and teams stay organized, prioritize tasks effectively, track progress, and adapt to changes, enhancing productivity and ensuring the timely completion of important work.

See more of what Teamwork.com can do for your business now - get started now for free, view our comprehensive pricing plans , or book a demo today.

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TABLE OF CONTENTS

• Write down all of your tasks
• Determine which tasks are truly important
• Stick to a schedule that reflects your priority tasks
• Tackle your most intense, high-effort tasks first
• Focus on one task at a time (hint: try not to multitask)
• Acknowledge what you realistically can and can’t get done 
• If possible, make prioritizing tasks a team-wide effort

The all-in-one platform for client work

Discover how Teamwork.com helps you drive business efficiency, grow profits, and scale confidently.

Brent Barnhart is a writer specializing in B2B content for marketing, SaaS, ecommerce, and small businesses.

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How to Prioritize Tasks Effectively at Work

• Increase Performance & Productivity
• Aug 29, 2023
• By Samuel Mbanaso

Do you wonder why you’re less productive no matter how many tasks you complete? Many of us have asked the same question. 

We mostly believe that burning both ends of the candle will yield results. But sadly, hard work does not guarantee productivity.

As it turns out, checking off tasks at the wrong time can be as problematic as procrastinating and idling around. That’s why you’ll find yourself still miles away from your goals despite being busy all day.

Your issue is not laziness, it’s poor prioritization. 

We lose focus on the things that matter most because we’re chasing deadlines and engaging in unnecessary activities.

Consider a McKinsey study that showed how workers spend their workweek. It reported that 20% of their typical workweek goes to tracking down colleagues for help or looking for internal information. The study also found that employees spend 28% of their workweek attending to emails.

Those numbers show how workers often misplace priorities, and you may be in the same boat.

However, by learning how to prioritize projects properly, you can turn things around and boost your productivity level. 

This article covers the steps to prioritize assignments at work to make the most of your time.

1. Create a Complete To-Do List

The first step in prioritizing at work is capturing all your tasks in one place. 

During this stage, you don’t have to worry about order. Your main goal is identifying and listing every task that you must handle.

This process may sound like what you do every day, but it’s not.

It involves adding everything up, from your ideas to planned meetings and conversations.

Collate your tasks from every source, including your boss, colleagues, external departments, clients, and partners. If you’re going to respond to emails or are expecting a project progress report to review, add those activities to your list.

This way, you have all you need to determine your priorities and order them. Leaving a task out of the list will mess up your schedule and disrupt your workflow when it surfaces later.

This central to-do list repository can cover each day, week, or month. But it’s important that you create master lists for each day to make things easy.

You can use a piece of paper, spreadsheet, checklist app , or project management tool to create the list. What’s important is having access to update the list when new information shows up.

2. Start Categorizing Urgent and Important Tasks

Once you’ve captured your assignments in one place, start categorizing them. Some projects are important and others are urgent. And you must understand that urgent and important tasks are not necessarily the same.

Understanding Urgent and Important Tasks

It’s crucial to understand the differences between urgent and important tasks in terms of priorities. This knowledge will help you determine which task to drop for the other. That’s because a pressing deadline shouldn’t be the only thing that drives you to complete a job.

Identifying Important Tasks

Identifying your important tasks follows building your master list and is the first step in pointing out your priorities.

Your long-term goals and objectives should help you pinpoint crucial tasks that should be taken seriously. 

For example, if your current project involves preparing a new proposal, tasks related to that project should be treated as more important.  

Categorizing Urgent Tasks

Urgent tasks have pressing deadlines and need swift attention. There are clear consequences when you don’t handle these tasks immediately, and you often can’t avoid them.

Which Task Should be Handled First?

Urgent tasks are not necessarily tied to your long-term goals and may be work you have to do for colleagues.

However, some important tasks can also be urgent.

In a case where an important action requires immediate attention, it should come first. But if important tasks are not urgent, they can be treated later. 

That said, it’s recommended that you favor important and non-urgent to-dos over urgent and non-important ones. That’s because you can delegate the latter to free up time for the former.

3. Use a Task Prioritization Technique

Your understanding of important and regent tasks will help you successfully implement the techniques we’ll explain below.

There are different time management and prioritization systems that will help you identify and order your priorities. Some will work better than others, depending on the nature of your activities. 

So, go through the different techniques and pick the ones that work best for you.

The 4 Ds of Time Management

One of the most effective ways to categorize your projects is using the 4 Ds of time management. They’re a series of actions that allow you to group tasks into four different buckets. This way, you can separate them according to importance and declutter the to-do list.

The actions are:

• Do : Urgent and important assignments that you must clear immediately. These should top the list.
• Defer : These projects can be put off for later.
• Delegate : Assignments and activities that you can hand off to other people.
• Delete : Tasks that you should remove from the list. 

Jobs that you must act on are those that are important and urgent. They mostly have pressing deadlines that attract severe consequences if not adhered to.

Deferring Tasks

Deferring tasks is different from procrastination . Putting off some jobs for later helps you clear your schedule to deal with more pressing and critical responsibilities that require urgent attention. 

Delaying tasks in this context can also help you maintain your work flow and current schedule. 

For example, if a colleague brings a new job to your desk, you can delay that job if it’s not critical. This way, you’ll not disrupt your original work process and schedule.

The important thing about the ‘defer’ process is accurately identifying tasks that you should complete later.

So, regardless of how interesting or easy a project seems to be, you should ideally defer it if it’s not important.

Delegating Tasks

While delegating tasks sheds your workload, it’s a sensitive undertaking. 

You may have to deal with the anxiety of not doing the job yourself, as the thought of errors can be frightening.

That’s why you have to be careful about delegating. 

But how do you hack it?

CEOs and managers use the 70 percent rule to identify responsibilities that others can handle, and it’s been effective. 

According to the rule, you should handover an activity to someone who will have at least a 70% success rate at executing it. The goal is to forego perfection while maintaining an acceptable level of quality.

That said, you should only apply the 70% rule to tasks that are suitable for this performance level. You have to handle those that require a 100% success rate yourself. 

Deleting Tasks

Some activities are unnecessary and end up being time-wasters. So, you have to get rid of them. This action is another way to clean up your to-do list and make time for meaningful work.

While the process may seem self-explanatory, you may encounter challenges when identifying to-dos you should delete.

The best way to use the “delete” technique is to look for ineffective and dispensable activities in your schedule.

For example, are you scheduled to attend meetings that you don’t have to join? Can the entire meeting agenda be covered in 20 minutes instead of 2 hours? And do you really have to organize a meeting for project reports when you can send and receive updates via email?

It would be good to take your time and reanalyze your work routine. You’ll discover tasks that you can delete or significantly shorten.

Eisenhower Matrix

The Eisenhower Matrix is a task prioritization technique developed by Dwight D. Eisenhower, a World War II general and the 34th President of the United States. 

The matrix is a straightforward tool that helps you simplify decision-making when scheduling and prioritizing responsibilities. 

It’s a quadrant that helps you differentiate levels of importance and urgency. That’s why it’s also called the Urgent-Important Matrix or Priority Matrix.

Tasks are split into four different boxes to allow you determine what comes first and what you should eliminate.

It’s different from the 4 Ds of Time Management because it clearly categorizes jobs according to importance, not nature.

Let’s break down the four quadrants.

Quadrant 1: Urgent and Important Tasks

These assignments have the highest priority level in the quadrant. You’ll incur serious consequences if you neglect them for others. So, lump activities that you have to complete as soon as possible into this category.

Quadrant 2: Important and Not Urgent

This quadrant is considered the most productive of the litter. Since they’re not urgent, you can easily decide to put them off. However, they provide more value in the long run when you get them done. So, they should ideally follow the important and urgent tasks in your schedule.

Quadrant 3: Urgent and Not Important

These tasks tend to cause confusion and drag down productivity. Most of the time, they’re interruptions that disrupt your workflow and remove your attention from what’s important. 

They’re mainly tasks that you do for others and don’t concern the core aspect of your work. Yet, they have pressing deadlines.

You can get rid of these by delegating them using the 70 percent rule as we explained above.

Quadrant 4: Not Urgent and Not Important

These are errands that you must always avoid. They’re the same as those that fall into the delete category in the 4 Ds of time management.

Order Your Tasks with Precision Using the Relative Priority Technique

No doubt, the Eisenhower Matrix is a great prioritization model that has worked for many. But, what if many tasks end up in the first quadrant?

The same goes for the Do category in the 4 Ds of Time Management. What happens when multiple jobs have the same level of importance and urgency?

That’s where the Relative Priority technique becomes beneficial.

The technique helps you weigh the importance and urgency of each task against another to determine which should come on top.

It involves assigning a number, from one to ten, to each activity. There can only be a single number one, two, three, and so on.

Now you know how the technique works, but how do you weigh tasks with similar priorities?

First, use each task’s deadline to determine where to start from. Then, check their dependencies.

For example, if a task must be completed to put others in motion, then you should increase its rank.

Focus on Your Most Important Tasks

What if you struggle to get through your tasks after prioritizing them? The MIT method can help.

Zen Habits’ Leo Babuta made the technique popular when he explained how it helped his work process.

How does it work?

Choose three of your most important to-dos and complete them first. Then, you can breeze through other jobs as a bonus for the day.

This way, you’ll get other jobs done without the added pressure. And things will also be easier since you’re relishing the high of completing your most important assignments.

Use the Pareto Principle

The Pareto Principle complements the MIT method and helps you find a way to schedule a successful work day.

It was named after Vilfredo Pareto and is also called the “Law of the vital few” or the 80/20 rule.

The principle proposes that 20% of your tasks determine 80% of your work day’s success.

But the tricky part of the technique is identifying 20% of your work.

So how do you go about it?

Look at your MIT.

Choose activities from your to-do list that, once completed, will make you feel accomplished for the day. 

Once you identify these jobs, start working on them and focus on one at a time.

Tips on Executing Prioritized Tasks and Sticking to Schedule

You’ve done the bulk of the work if you’ve prioritized your responsibilities and figured out how to move from one to another. 

But it’s one thing to know your priorities and it’s another to execute them on time.

The tips that follow will show you how to manage your priorities and go through your daily schedule with ease.

Track Your Time

Most of us are victims of the planning fallacy – a situation where we underestimate how much time it will take to complete an assignment. As a result, we end up chasing deadlines almost every time.

This situation happens because we don’t measure time.

When you frequently measure time, you begin to really understand and appreciate each task’s difficulty level.

That’s why you should use a time tracking application to assess your performance. 

Time trackers monitor how much time you spend on a task, application, or website. Once you complete the job, you can check the detailed analysis to understand the time it consumed.

Time tracking also motivates you to do better since it shows you apps and websites that distract you. Also, knowing you’re on the clock improves your sense of urgency and focus.

Break Large Tasks Into Smaller Chunks

“There is only one way to eat an elephant: a bite at a time.” – Desmond Tutu

Breaking up large tasks into smaller manageable pieces allows you to prioritize better and avoid procrastination and mental fatigue.

And studies have shown that procrastination has a negative impact on performance and costs workers a huge chunk of their salaries.

To avoid the overwhelming feeling that comes with looking at a large task, break it down.

To do that, make up a to-do list out of the large task and assign deadlines to each. Then, integrate those tasks into your schedule. 

You can also turn these tasks into milestones, so you can keep your eyes on the big prize. This way, you get a sense of satisfaction each time you inch closer to your objective.

Avoid Multitasking

Multitasking is the merchant of distractions. And distractions are deadly to productivity and performance. 

You’ll hardly get a lot done if you decide to tackle two priorities simultaneously. A study tested 200 people in dual- and single-tasking conditions and found that 97.5 percent of people find it hard to multitask.

So, make sure you stick to the Relative Priorities technique explained above and cross off each to-do at a time.

The following tips can also help you avoid multitasking :

• Focus on your work when at your desk
• Remove potential distractions from your workspace
• Turn off unnecessary notifications
• Block distracting applications and websites using app blockers

Map Your Time

Time mapping is a time management system that helps you move from task to task with ease.

After ranking your priorities, you may be swayed by distractions or impromptu projects. By using a time map, you always know what to do next, keeping you grounded and focused on your schedule.

The time map shows you an overview of your entire day, week, or month. This way, you’re able to see how you move across the map.

Creating a time map is easy. You can use a sheet of paper or digital calendar to create a time block for each task. Then use color codes to categorize these tasks and activities according to type and priority.

Avoid Distractions

Distractions can make you take your eye off the ball and fall behind schedule. According to a University of California, Irvine study , it can take up to 23 minutes to get back to work after an interruption.

So, try avoiding distractions to focus on your jobs and clear off your tasks when you should.

So how do you reduce distractions?

• Block access to social media and other distracting websites to reduce cyberslacking
• Take short breaks
• Delegate non-work related tasks, especially if you work from home
• Use visual reminders such as sticky notes to tether yourself to work
• Plan your day the night before and stick to your schedule
• Declutter your workspace
• Then off notifications

The Pomodoro Technique Can Help

The Pomodoro Technique is an efficient and popular time management method that improves concentration and reduces mental stress. Pomodoro is the Italian word for tomato and this technique is named after the tomato-shaped kitchen timer that was originally used to alternate short work bursts with breaks.

Here’s how it works:

First Step : Set a 25-minute timer for your first task

Second Step : Work on the task and stop once the time is up

Third Step : Take a 5-minute break

Fourth Step : Repeat, then after the 4th pomodoro take a longer 20- to 30-minute break

Wrapping Up

Now you know that you can do 20% of the day’s tasks and still be productive. The secret is knowing the right items to cross off your to-do list. Continue refining your process to discover the best way to prioritize your goals and execute them.

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Optimal priority assignment for real-time systems: a coevolution-based approach

• Open access
• Published: 06 August 2022
• Volume 27 , article number  142 , ( 2022 )

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• Jaekwon Lee 1 , 2 ,
• Seung Yeob Shin   ORCID: orcid.org/0000-0001-9025-7173 1 ,
• Shiva Nejati 1 , 2 &
• Lionel C. Briand 1 , 2  

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In real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems from different domains and several synthetic systems. The results indicate that our approach significantly outperforms both our baselines, i.e., random search and sequential search, and solutions defined by practitioners. Our approach scales to complex industrial systems as an offline analysis method that attempts to find near-optimal solutions within acceptable time, i.e., less than 16 hours.

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1 Introduction

Mission-critical systems are found in many different application domains, such as aerospace, automotive, and healthcare domains. The success of such systems depends on both functional and temporal correctness. For functional correctness, systems are required to provide appropriate outputs in response to the corresponding stimuli. Regarding temporal correctness, systems are supposed to generate outputs within specified time constraints, often referred to as deadlines. The systems that have to comply with such deadlines are known as real-time systems (Liu 2000 ). Real-time systems typically run multiple tasks in parallel and rely on a real-time scheduling policy to decide which tasks should have access to processing cores, i.e., CPUs, at any given time.

While developing a real-time system, one of the most common problems that engineers face is the assignment of priorities to real-time tasks in order for the system to meet its deadlines. Based on priorities of real-time tasks, the system’s task scheduler determines a particular order for allocating real-time tasks to processing cores. Hence, a priority assignment that is poorly designed by engineers makes the system scheduler execute tasks in an order that is far from optimal. In addition, the system will likely violate its performance and time constraints, i.e., deadlines, if a poor priority assignment is used.

In real-time systems, the problem of optimally assigning priorities to tasks is important not only to avoid deadline misses but also to maximize safety margins from task deadlines and is subject to engineering constraints . Tasks may exceed their expected execution times due to unexpected interrupts. For example, it is infeasible to test an aerospace system exhaustively on the ground such that potential environmental uncertainties, e.g., those related to space radiations, are accounted for. Hence, engineers assign optimal priorities to tasks such that the remaining times from tasks’ completion times to their deadlines, i.e., safety margins, are maximized to cope with potential uncertainties. Furthermore, engineers typically have to account for additional engineering constraints, e.g., they assign higher priorities to critical tasks that must always meet their deadlines compared to the tasks that are less critical or non-critical.

A brute force approach to find an optimal priority assignment would have to examine all n ! distinct priority assignments, where n denotes the number of tasks. Furthermore, for a given priority assignment, schedulability analysis is, in general, known as a hard problem (Audsley 2001 ), which determines whether or not tasks will always complete their executions within their specified deadlines. Thus, optimizing priority assignments is also a hard problem because the space of all possible system states to explore in order to find optimal priority assignments is very large. Most of the prior works on optimizing priority assignments provide analytical methods (Fineberg and Serlin 1967 ; Leung and Whitehead 1982 ; Audsley 1991 ; Davis and Burns 2007 ; Chu and Burns 2008 ; Davis and Burns 2009 ; Davis and Bertogna 2012 ), which rely on well-defined system models and are very restrictive. For example, they assume that tasks are independent, i.e., tasks do not share resources (Davis et al. 2016 ; Zhao and Zeng 2017 ). Industrial systems, however, are typically not compatible with such (simple) system models. In addition, none of the existing work addresses the problem of optimizing priority assignments by simultaneously accounting for multiple objectives, such as safety margins and engineering constraints, as discussed above.

Search-based software engineering (SBSE) has been successfully applied in many application domains, including software testing (Wegener et al. 1997 ; Wegener and Grochtmann 1998 ; Lin et al. 2009 ; Arcuri et al. 2010 ; Shin et al. 2018 ), program repair (Weimer et al. 2009 ; Tan et al. 2016 ; Abdessalem et al. 2020 ), and self-adaptation (Andrade and Macêdo 2013 ; Chen et al. 2018 ; Shin et al. 2020 ), where the search spaces are very large. Despite the success of SBSE, engineering problems in real-time systems have received much less attention in the SBSE community. In the context of real-time systems, there exists limited work on finding stress test scenarios (Briand et al. 2005 ) and predicting worst-case execution times (Lee et al. 2020b ), which complements our work.

In practice, priority assignments result from an interactive process between the development and testing teams. While developing a real-time system, developers assign priorities to real-time tasks in the system and then testers stress the system to check whether or not the system meets its specified deadlines. If testers find a problematic condition under which any of the tasks violates its deadline, developers have to modify the priority assignment to address the problem. The back-and-forth between the development and testing teams continues until a priority assignment that does not lead to any deadline miss is found or the one that yields the least critical deadline misses is identified. The process is, however, not automated.

In this article, we use metaheuristic search algorithms to automate the process of assigning priorities to real-time tasks. To mimic the interactive back-and-forth between the development and testing teams, we use competitive coevolutionary algorithms (Luke 2013 ). Coevolutionary algorithms are a specialized class of evolutionary search algorithms. They simultaneously coevolve two populations (also called species) of (candidate) solutions for a given problem. They can be cooperative or competitive. Such competitive coevolution is similar to what happens in nature between predators and preys. For example, faster preys escape predators more easily, and hence they have a higher probability of generating offspring. This impacts the predators, because they need to evolve as well to become faster if they want to feed and survive (Meneghini et al. 2016 ). Hence, the two species, i.e., predators and preys, have coevolved competitively. We note that no species has the competing traits of predators and preys simultaneously as such species could not evolve to survive. In our context, priority assignments defined by developers can be seen as preys and stress test scenarios as predators. The priority assignments need to evolve so that stress testing is not able to push the system into breaking its real-time constraints. Dually, stress test scenarios should evolve to be able to break the system when there is a chance to do so.

Contributions.

We propose an O ptimal P riority A ssignment M ethod for real-time systems (OPAM). Specifically, we apply multi-objective, two-population competitive coevolution (Popovici et al. 2012 ) to address the problem of finding near-optimal priority assignments, aiming at maximizing the magnitude of safety margins from deadlines and constraint satisfaction. In OPAM, two species relate to priority assignment and stress testing coevolve synchronously, and compete against each other to find the best possible solutions. We evaluated OPAM by applying it to six complex, industrial systems from different domains, including the aerospace, automotive, and avionics domains, and several synthetic systems. Our results show that: (1) OPAM finds significantly better priority assignments compared to our baselines, i.e., random search and sequential search, (2) the execution time of OPAM scales linearly with the number of tasks in a system and the time required to simulate task executions, and (3) OPAM priority assignments significantly outperform those manually defined by engineers based on domain expertise.

We note that OPAM is the first attempt to apply coevolutionary algorithms to address the problem of priority assignment. Further, it enables engineers to explore trade-offs among different priority assignments with respect to two objectives: maximizing safety margins and satisfying engineering constraints. Our full evaluation package is available online (Lee et al. 2021 ).

Organization.

The remainder of this article is structured as follows: Section  2 motivates our work. Section  3 defines our specific problem of priority assignment in practical terms. Section  4 discusses related work. Sections  5 and  6 describe OPAM. Section  7 evaluates OPAM. Section  8 concludes this article.

2 Motivating case study

We motivate our work using an industrial case study from the satellite domain. Our case study concerns a mission-critical real-time satellite, named ESAIL (LuxSpace 2021 ), which has been developed by LuxSpace – a leading system integrator for microsatellites and aerospace system. ESAIL tracks vessels’ movements over the entire globe as the satellite orbits the earth. The vessel-tracking service provided by ESAIL requires real-time processing of messages received from vessels in order to ensure that their voyages are safe with the assistance of accurate, prompt route provisions. Also, as ESAIL orbits the planet, it must be oriented in the proper position on time in order to provide services correctly. Hence, ESAIL’s key operations, implemented as real-time tasks, need to be completed within acceptable times, i.e., deadlines.

Engineers at LuxSpace analyze the schedulability of ESAIL across different development stages. At an early design stage, the engineers use a priority assignment method that extends the rate monotonic scheduling policy (Fineberg and Serlin 1967 ), which is a theoretical priory assignment algorithm used in real-time systems. At a later development stage, if the engineers found that any real-time task of ESAIL cannot complete its execution within its deadline, the engineers, in our study context, reassign priorities to tasks in order to address the problem of deadline violations.

The rate monotonic policy assigns priorities to tasks that arrive to be executed periodically and must be completed within a certain amount of time, i.e., periodic tasks with hard deadlines. According to the policy, periodic tasks that arrive frequently have higher priorities than those of other tasks that arrive rarely. In ESAIL, for example, if the vessel-tracking task arrives every 100ms and the satellite-position control task arrives every 150ms, the former has a higher priority than the latter. However, the rate monotonic policy does not account for tasks that arrive irregularly and should be completed within a reasonable amount of time, i.e., aperiodic tasks with soft deadlines. ESAIL contains aperiodic tasks with soft deadlines as well, such as a task for updating software. Hence, the engineers extend the rate monotonic policy to assign priorities to all tasks of ESAIL. The extensions are as follows: First, the engineers assign priorities to periodic tasks based on the rate monotonic policy. Second, the engineers assign lower priorities to aperiodic tasks than those of periodic tasks. As aperiodic tasks with soft deadlines are typically considered less critical than periodic tasks with hard deadlines, the engineers aim to ensure that periodic tasks complete their executions within their deadlines by assigning lower priorities to aperiodic tasks while periodic tasks have higher priority. Engineers use a heuristic to assign priorities to aperiodic tasks. They treat aperiodic tasks as (pseudo-)periodic tasks by setting aperiodic tasks’ (expected) minimum arrival rates as their fixed arrival periods, making the tasks frequently arrive. The engineers then apply the rate monotonic policy for the aperiodic tasks with the synthetic periods while ensuring that aperiodic tasks have lower priorities than those of periodic tasks.

A priority assignment made at an early design stage keeps changing while developing ESAIL due to various reasons, such as changes in requirements and implementation constraints. At a development stage, instead of relying on the extended rate monotonic policy, the engineers assign priorities based on their domain expertise, manually inspecting schedulability analysis results. Hence, a priority assignment at later development stages often does not follow the extended rate monotonic policy. For example, as aperiodic tasks are also expected to be completed within a reasonable amount of time, some aperiodic tasks may have higher priorities than some periodic tasks as long as they are schedulable.

Engineers at LuxSpace, however, are still faced with the following issues: (1) Their priority assignment method, which extends the rate monotonic scheduling policy, assigns priorities to tasks in order to ensure only that tasks are to be schedulable. However, engineers have a pressing need to understand the quality of priority assignments in detail as they impact ESAIL operations differently. For example, once ESAIL is launched into orbit, the satellite operates in the space environment, which is inherently impossible to be fully tested on the ground. Unexpected space radiations may trigger unusual system interrupts, which hasn’t been observed on the ground, resulting in overruns of ESAIL tasks’ executions. In such cases, a priority assignment assessed on the ground may not be able to tolerate such unexpected uncertainties. Hence, engineers need a priority assignment that enables ESAIL tasks to tolerate unpredictable uncertainties as much as possible and to be schedulable. (2) Engineers at LuxSpace assign priorities to tasks without any systematic assistance. Instead, they rely on their expertise and the current practices described above to manually assign priorities to ensure that tasks are to be schedulable. To this end, we are collaborating with LuxSpace to develop a solution for addressing these issues in assigning task priority.

3 Problem description

This section defines the task, scheduler, and schedulability concepts, which extend the concepts defined in our previous work (Lee et al. 2020b ) by augmenting our previous definitions with the notions of safety margins, constraints in assigning priorities, and relationships between real-time tasks. We then describe the problem of optimizing priority assignments such that we maximize the magnitude of safety margins and the degree of constraint satisfaction. Figure  1 shows an overview of the conceptual model that represents the key abstractions required to analyze optimal priority assignments for real-time systems. The entities in the conceptual model are described below.

A conceptual model representing the key abstractions to analyze optimal priority assignments

We denote by j a real-time task that should complete its execution within a specified deadline after it is activated (or arrived). Every real-time task j has the following properties: priority denoted by p r ( j ), deadline denoted by d l ( j ), and worst-case execution time (WCET) denoted by w c e t ( j ). Task priority p r determines if an execution of a task is preempted by another task. Typically, a task j preempts the execution of a task \(j^{\prime }\) if the priority of j is higher than the priority of \(j^{\prime }\) , i.e., \({pr}(j) > {pr}(j^{\prime })\) . The p r ( j ) priority is a fixed value assigned to task j . Such fixed priorities are determined offline; hence, they are not changed online for any reason. Note that a real-time task scheduler that relies on fixed priorities is applied in all the study subjects in this article (see Section  7.2 ) and is commonly used in industrial systems (Briand et al. 2005 ; Guan et al. 2009 ; Lin et al. 2009 ; Anssi et al. 2011 ; Zeng et al. 2014 ; Di Alesio et al. 2015 ; Du̇rr et al. 2019 ; Lee et al. 2020a ).

The d l ( j ) function determines the deadline of a task j relative to its arrival time. A task deadline can be either hard or soft . A hard deadline of a task j constrains that j must complete its execution within a deadline d l ( j ) after j is activated. While violations of hard deadlines are not acceptable, depending on the operating context of a system, violating soft deadlines may be to some extent tolerated. Note that we use a metaheuristic search relying on fitness functions quantifying the degrees of deadline misses, safety margins, and constraint satisfaction. Such functions do not depend on the nature of the deadlines. Our approach outputs a set of priority assignments that are Pareto optimal with respect to safety margins and constraint satisfaction. Engineers then perform domain-specific trade-off analysis among Pareto solutions. Hence, in this article, we handle hard and soft deadline tasks in the same manner.

Real-time tasks are either periodic or aperiodic . Periodic tasks, which are typically triggered by timed events, are invoked at regular intervals specified by their period . We denote by p d ( j ) the period of a periodic task j , i.e., a fixed time interval between subsequent activations (or arrivals) of j . Any task that is not periodic is called aperiodic. Aperiodic tasks have irregular arrival times and are activated by external stimuli which occur irregularly. In real-time analysis, based on domain knowledge, we typically specify a minimum inter-arrival time denoted by p m i n ( j ) and a maximum inter-arrival time denoted by p m a x ( j ) indicating the minimum and maximum time intervals between two consecutive arrivals of an aperiodic task j . In real-time analysis, sporadic tasks are often separately defined as having irregular arrival intervals and hard deadlines (Liu 2000 ). In our conceptual definitions, however, we do not introduce new notations for sporadic tasks because the deadline and period concepts defined above sufficiently characterize sporadic tasks. Note that for periodic tasks j , we have p m i n ( j ) = p m a x ( j ) = p d ( j ). Otherwise, for aperiodic tasks j , we have p m a x ( j ) > p m i n ( j ).

Task relationships.

The execution of a task j depends not only on its own parameters described above, e.g., priority p r ( j ) and period p d ( j ), but also on its relationships with other tasks. Relationships between tasks are typically determined by task interactions related to accessing shared resources and triggering arrivals of other tasks (Di Alesio et al. 2012 ). Specifically, if two tasks j and \(j^{\prime }\) access a shared resource r in a mutually exclusive way, j may be blocked from executing for the period during which \(j^{\prime }\) accesses r . We denote by \({dp}(j,j^{\prime })\) the resource-dependency relation between tasks j and \(j^{\prime }\) that holds if j and \(j^{\prime }\) have mutually exclusive access to a shared resource r such that they cannot be executed in parallel or preempt each other, but one can execute only after the other has completed accessing r .

The other type of relationship between tasks is related to a task j triggering the arrival of another task \(j^{\prime }\) . This is a common interaction between tasks (Locke et al. 1990 ; Anssi et al. 2011 ; Di Alesio et al. 2015 ). For example, j may hand over some of its workload to \(j^{\prime }\) due to performance or reliability reasons. We denote by \({tr}(j,j^{\prime })\) the triggering relation between tasks j and \(j^{\prime }\) that holds if j triggers the arrival of \(j^{\prime }\) . We note that both relationships are defined at the level of tasks, following prior works (Locke et al. 1990 ; Anssi et al. 2011 ; Di Alesio et al. 2015 ) describing the five industrial case study systems used in our experiments (see Section  7.2 ).

Let J be a set of tasks to be scheduled by a real-time scheduler. A scheduler then dynamically schedules executions of tasks in J according to the tasks’ arrivals and the scheduler’s scheduling policy over the scheduling period \(\mathbb {T} = [0,\mathbf {T}]\) . We denote by a t k ( j ) the k th arrival time of a task j ∈ J . The first arrival of a periodic task j does not always occur immediately at the system start time (0). Such offset time from the system start time to the first arrival time a t 1 ( j ) of j is denoted by o f f s e t ( j ). For a periodic task j , the k th arrival of j within \(\mathbb {T}\) is a t k ( j ) ≤ T and is computed by a t k ( j ) = o f f s e t ( j ) + ( k − 1) ⋅ p d ( j ). For an aperiodic task \(j^{\prime }\) , \({at}_{k}(j^{\prime })\) is determined based on the k − 1th arrival time of \(j^{\prime }\) and its minimum and maximum arrival times. Specifically, for k > 1, \({at}_{k}(j^{\prime }) \in [{at}_{k-1}(j^{\prime })+{pmin}(j^{\prime }), {at}_{k-1}(j^{\prime })+{pmax}(j^{\prime })]\) and, for k = 1, \({at}_{1}(j^{\prime }) \in [{pmin}(j^{\prime }), {pmax}(j^{\prime })]\) , where \({at}_{k}(j^{\prime }) < \mathbf {T}\) .

A scheduler reacts to a task arrival at a t k ( j ) by scheduling the execution of j . Depending on a scheduling policy (e.g., rate monotonic scheduling policy for single-core systems (Fineberg and Serlin 1967 ) and single-queue multi-core scheduling policy (Arpaci-Dusseau and Arpaci-Dusseau 2018 )), an arrived task j may not start its execution at the same time as it arrives when higher priority tasks are executing on all processing cores. Also, task executions may be interrupted due to preemption. We denote by e t k ( j ) the completion time for the k th arrival of a task j . According to the worst-case execution time of a task j , we have: e t k ( j ) ≥ a t k ( j ) + w c e t ( j ).

During system operation, a scheduler generates a schedule scenario which describes a sequence of task arrivals and their completion time values. We define a schedule scenario as a set S of tuples ( j , a t k ( j ), e t k ( j )) indicating that a task j has arrived at a t k ( j ) and completed its execution at e t k ( j ). Due to a degree of randomness in task execution times and aperiodic task arrivals, a scheduler may generate a different schedule scenario for different runs of a system.

Figure  2 shows two schedule scenarios S (Figure  2a ) and \(S^{\prime }\) (Figure  2b ) produced by a scheduler over the [0,23] time period of a system run. Both S and \(S^{\prime }\) describe executions of three tasks, j 1 , j 2 , and j 3 arrived at the same time stamps (see a t i in the figures). In both scenarios, the aperiodic task j 1 is characterized by: p m i n ( j 1 ) = 5, p m a x ( j 1 ) = 13, d l ( j 1 ) = 4, and w c e t ( j 1 ) = 2. The aperiodic task j 2 is characterized by: p m i n ( j 2 ) = 3, p m a x ( j 2 ) = 10, d l ( j 2 ) = 4, and w c e t ( j 2 ) = 1. The periodic task j 3 is characterised by: p d ( j 3 ) = 8, d l ( j 3 ) = 7, and w c e t ( j 3 ) = 3. The priorities of the three tasks in S (resp. \(S^{\prime }\) ) satisfy the following: p r ( j 1 ) > p r ( j 2 ) > p r ( j 3 ) (resp. p r ( j 2 ) > p r ( j 3 ) > p r ( j 1 )). In both scenarios, task executions can be preempted depending on their priorities. Then, S is defined by S = {( j 1 ,5,7), …, ( j 2 ,4,5), …, ( j 3 ,8,14), ( j 3 ,16,19))}; and \(S^{\prime }\) is defined by \(S^{\prime } = \{(j_{1}, 5, 7)\) , …, ( j 2 ,4,5), …, ( j 3 ,8,12), ( j 3 ,16,19))}.

Example schedule scenarios S and \(S^{\prime }\) of three tasks: j 1 , j 2 , and j 3 . (a) The S schedule scenario is produced when p r ( j 1 ) = 3, p r ( j 2 ) = 2, and p r ( j 3 ) = 1. (b) The \(S^{\prime }\) schedule scenario is produced when p r ( j 1 ) = 1, p r ( j 2 ) = 3, and p r ( j 3 ) = 3

Schedulability.

Given a schedule scenario S , a task j is schedulable if j completes its execution before its deadline, i.e., for all e t k ( j ) observed in S , e t k ( j ) ≤ a t k ( j ) + d l ( j ). Let J be a set of tasks to be scheduled by a scheduler. A set J of tasks is then schedulable if for every schedule S of J , we have no task j ∈ J that misses its deadline.

As shown in schedule scenarios S and \(S^{\prime }\) presented in Fig.  2a and b, respectively, all three tasks, j 1 , j 2 , and j 3 , are schedulable. However, we note that the overall amounts of remaining time, i.e., safety margins, from the tasks’ completions to their deadlines observed in S and \(S^{\prime }\) are different (see the second completion times and deadlines of j 1 , j 2 , and j 3 in S and \(S^{\prime }\) ) because S and \(S^{\prime }\) are produced by using different priority assignments. Engineers typically desire to assign optimal priorities to real-time tasks that aim at maximizing such safety margins, as discussed below.

In real-time systems, fixed priorities are typically assigned to tasks (Davis et al. 2016 ; Lee et al. 2020a ). Finding an appropriate priority assignment is important not only for ensuring the schedulability of a system but also for maximizing the safety margins within which a system can tolerate unexpected execution time overheads. For example, if an unpredictable error occurs and triggers check-point mechanisms (Davis and Burns 2007 ), which re-execute part or all of a task j , then the execution time of j unexpectedly overruns. Hence, engineers need an optimal priority assignment that maximizes the overall remaining times from task completion times to task deadlines, i.e., safety margins.

While assigning priorities to tasks, engineers also account for constraints, that are often but not always domain-specific. For example, aperiodic tasks’ priorities should be lower than those of periodic tasks because periodic tasks are often more critical than aperiodic tasks. Hence, engineers develop a system that prioritizes executions of periodic tasks over aperiodic tasks. Recall from Section  2 , this constraint is desirable by engineers. When needed, however, engineers can violate the constraint to some extent in order to ensure that aperiodic tasks complete within a reasonable amount of time while periodic tasks meet their deadlines. Constraints can be either hard constraints, which must be satisfied, or soft constraints, which are desired to be satisfied. In our study, hard constraints need to be assured while scheduling tasks, e.g., a running task’s priority must be higher than a ready task’s priority, which are enforced by a scheduler. In the context of optimizing priority assignments, we focus on maximizing the extent of satisfying soft constraints. We refer to a soft constraint as a constraint in this paper.

Our work aims at optimizing priority assignments that maximize the safety margins while satisfying such constraints. Specifically, for a set J of tasks to be analyzed, we define three concepts as follows: (1) a priority assignment for J denoted by \(\rightarrow {P} \) , (2) the magnitude of safety margins for a priority assignment \(\rightarrow {P}\) denoted by \({fs}(\rightarrow {P})\) , and (3) the degree of constraint satisfaction denoted by \({fc}(\rightarrow {P})\) . We note that Section  6.3 describes how we optimize \(\rightarrow {P}\) , and compute \({fs}(\rightarrow {P})\) and \({fc}(\rightarrow {P})\) in detail. Our study aims at finding a set B of best possible priory assignments that are Pareto optimal (Knowles and Corne 2000 ) such that a priority assignment \(\rightarrow {P} \in \mathbf {B}\) maximizes both \({fs}(\rightarrow {P})\) and \({fc}(\rightarrow {P})\) , and any other priority assignments in B are equally viable.

4 Related Work

This section discusses related research strands in the areas of priority assignments, real-time analysis using exhaustive techniques, search-based analysis in real-time systems, and coevolutionary analysis in software engineering.

Priority assignment.

The problem of optimally assigning priorities to real-time tasks has been widely studied (Fineberg and Serlin 1967 ; Liu and Layland 1973 ; Leung and Whitehead 1982 ; Audsley 1991 ; Tindell et al. 1994 ; George et al. 1996 ; Audsley 2001 ; Davis and Burns 2007 ; Chu and Burns 2008 ; Davis and Burns 2009 ; 2011 ; Davis and Bertogna 2012 ; Davis et al. 2016 ; Zhao and Zeng 2017 ; Hatvani et al. 2018 ). Fineberg and Serlin ( 1967 ) reported early work that relies on a simple system model, assuming, for example, that all tasks arrive periodically, tasks run on a single processing core, tasks’ deadlines are equal to their periods, and task executions are independent from one another. They proposed a priority assignment method, named rate-monotonic priority ordering (RMPO), that assigns higher priorities to the tasks with shorter periods. RMPO can find a feasible priority assignment that guarantees periodic tasks to be schedulable when such priority assignments exist (Liu and Layland 1973 ). Leung and Whitehead ( 1982 ) extended RMPO to relax one of the underlying assumptions made in RMPO. Specifically, their priority assignment approach, known as deadline-monotonic priority ordering (DMPO), accounts for task deadlines that can be less than or equal to their periods. In contrast to our work, however, these methods are often not applicable to industrial systems that are not compatible with their simplified system models. Recall from Section  3 that a realistic system typically consists of both periodic and aperiodic tasks. Task executions depend on their relationships, i.e., resource dependencies and triggering relationships, with other tasks.

Audsley ( 2001 ) designed a priority assignment method, named optimal priority assignment (OPA), that relies on an existing schedulability analysis method M . OPA guarantees to find a feasible priority assignment that is schedulable according to M if such priority assignments exist. OPA is applicable to more complex systems than those supported by the methods mentioned above, i.e., RMPO and DMPO. Specifically, OPA can find a feasible priority assignment even in the following situations: (1) First arrivals of periodic tasks occur after some offset time (Audsley 1991 ). (2) Aperiodic tasks have arbitrary deadlines (Tindell et al. 1994 ). (3) Task executions are scheduled based on a non-preemptive scheduling policy (George et al. 1996 ). (4) Tasks run on multiple processing cores (Davis and Burns 2011 ). Unlike our approach that accounts for two objectives, safety margins and engineering constraints (see Section  3 ), OPA attempts to find a feasible priority assignment whose only objective is to make all tasks schedulable. Note that such a feasible priority assignment does not necessarily maximize safety margins as discussed in Section  3 . Hence, a feasible priority assignment obtained by OPA is often fragile and sensitive any changes in task executions and unable to accommodate unexpected overheads in task execution times, which are commonly observed in industrial systems (Davis and Burns 2007 ).

OPA has been extended by several works (Davis and Burns 2007 ; Chu and Burns 2008 ; Davis and Burns 2009 ; Davis and Bertogna 2012 ). Davis and Burns ( 2007 ) presented a robust priority assignment method (RPA) with a degree of tolerance for unexpected overruns of task execution times. Chu and Burns ( 2008 ) introduced an extended OPA algorithm (OPA-MLD) that minimizes the lexicographical distance between the desired priority assignment and the one obtained by the algorithm. OPA-MLD enables important tasks to have higher priorities. Davis and Bertogna ( 2012 ) proposed an RPA extension (FNR-PA) to make RPA work when a system allows task preemption to be deferred for some interval of time. Davis and Burns ( 2009 ) developed a probabilistic robust priority assignment method (PRPA) for a real-time system to be less likely to violate its deadlines. Even though the prior works mentioned above improve OPA to some extent, they assume that task executions are independent of one another. In contrast to these existing approaches, OPAM accounts for dependencies among task executions, i.e., resource dependencies and triggering relationships (see our problem description in Section  3 ).

Some recent priority assignment techniques address scalability. Hatvani et al. ( 2018 ) presented an optimal priority and preemption-threshold assignment algorithm (OPTA) that attempts to decrease the computation time for finding a feasible priority assignment. OPTA uses a heuristic to traverse a problem space while pruning infeasible paths to efficiently and effectively explore the problem space. Zhao and Zeng ( 2017 ) introduced an effective priority assignment framework (EPAF) that combines a commercial solver for integer linear programs and their problem-specific optimization algorithm. However, these methods rely on simple system models that assume, for example, task executions to be independent and running on a single processing core. Therefore, the applicability of these techniques is limited. In contrast, recall from Sections  2 and  3 that our approach aims at scaling to complex industrial systems while accounting for realistic system characteristics regarding task periods, inter-arrival times, resource dependencies, triggering relationships, and multiple processing cores.

Table  1 compares our work, OPAM, with the other priority assignment techniques mentioned above. As shown in the table, we note that prior works rely on system models that are very restrictive. In particular, existing work assumes that task executions are independent of one another. However, task dependencies such as resource dependencies and triggering relationships are commonly observed in industrial systems. In addition, we note that no existing solution simultaneously accounts for safety margins and engineering constraints. Hence, to our knowledge, OPAM is the first attempt to provide engineers with a set of equally viable priority assignments, allowing trade-off analysis with respect to the two objectives: maximizing safety margins and satisfying engineering constraints.

Real-time analysis using exhaustive techniques.

Constraint programming and model checking have been applied to conclusively and exhaustively verify whether or not a system meets its deadlines (Kwiatkowska et al. 2011 ; Di Alesio et al. 2012 ; Nejati et al. 2012 ; Di Alesio et al. 2013 ). Existing research on priority assignment based on OPA rely on such exhaustive techniques to prove the schedulability of a set of tasks for a given priority assignment. We note that schedulability analysis is, in general, an NP-hard problem (Davis et al. 2016 ) that cannot be solved in polynomial time. As a result, exhaustive techniques based on model checking and constraint solving are often not amenable to analyze large industrial systems such as ESAIL – our motivating case study system – described in Section  2 . To assess if exhaustive techniques could scale to ESAIL, as discussed in Section  7.8 , we performed a preliminary experiment using UPPAAL (Behrmann et al. 2004 ), a model checker for real-time systems. We observed that UPPAAL was not able to verify schedulability of ESAIL tasks for a fixed priority assignment even after letting it run for several days (see Section  7.8 for more details).

Search-based analysis in real-time systems.

In real-time systems, most of the existing works that use search-based techniques focus on testing (Wegener et al. 1997 ; Wegener and Grochtmann 1998 ; Briand et al. 2005 ; Lin et al. 2009 ; Arcuri et al. 2010 ). Wegener et al. ( 1997 , 1998 ) introduced a testing approach based on a genetic algorithm that aims to check computation time, memory usage, and task synchronization by analyzing the control flow of a program. Briand et al. ( 2005 ) applied a genetic algorithm to find stress test scenarios for real-time systems. Lin et al. ( 2009 ) proposed a search-based approach to check whether a real-time system meets its timing and security constraints. Arcuri et al. ( 2010 ) presented a black-box system testing approach based on a genetic algorithm. Beyond testing real-time systems, Nejati et al. ( 2013 , 2014 ) developed a search-based trade-off analysis technique that helps engineers balance the satisfaction of temporal constraints and keeping the CPU time usage at an acceptable level. Lee et al. ( 2020b ) combined a search algorithm and machine learning to estimate safe ranges of worst-case task execution times within which tasks likely meet their deadlines. In contrast to these prior works, OPAM addresses the problem of optimally assigning priorities to real-time tasks while accounting for multiple objectives regarding safety margins and engineering constraints, thus enabling Pareto (trade-off) analysis. Further, OPAM uses a multi-objective, competitive coevolutionary search algorithm, which has been rarely applied to date in prior studies of real-time systems, as discussed next.

Coevolutionary analysis in software engineering.

Despite the success of search-based software engineering (SBSE) in many application domains including software testing (Wegener et al. 1997 ; Wegener and Grochtmann 1998 ; Lin et al. 2009 ; Arcuri et al. 2010 ; Shin et al. 2018 ), program repair (Weimer et al. 2009 ; Tan et al. 2016 ; Abdessalem et al. 2020 ), and self-adaptation (Andrade and Macêdo 2013 ; Chen et al. 2018 ; Shin et al. 2020 ), coevolutionary algorithms have been applied in only a few prior studies (Wilkerson and Tauritz 2010 ; Wilkerson et al. 2012 ; Boussaa et al. 2013 ). Wilkerson et al. ( 2010 , 2012 ) present a coevolution-based approach to automatically correct software. Their work introduced a program representation language to facilitate their automated corrections. (Boussaa et al. 2013 ) developed a code-smells detection approach. The main idea is to evolve two competing populations of code-smell detection rules and artificial code-smells. Unlike these prior works, we study the problem of optimally assigning priorities to tasks in real-time systems. To our knowledge, we are the first to address the priority assignment problem using a multi-objective, competitive coevolutionary search algorithm.

5 Approach Overview

Finding an optimal priority assignment is an inherently interactive process. In practice, once engineers assign priorities to the real-time tasks in a system, testers then stress the system to find a condition, i.e., a particular sequence of task arrivals, in which a task execution violates its deadline. Testers typically use a simulator or hardware equipment to stress the system by triggering plausible worst-case arrivals of tasks that maximize the likelihood of deadline misses. If testers find task arrivals that induce deadline misses, the task arrivals are reported to engineers in order to fix the problem by reassigning priorities. This interactive process of assigning priorities and testing schedulability continues until both engineers and testers ensure that the tasks meet their deadlines.

For such intrinsically interactive problem-solving domains, we conjecture that coevolutionary algorithms are potentially suitable solutions. A coevolutionary algorithm is a search algorithm that mutually adapts one of different species, e.g., in our study, two populations of priority assignments and task-arrival sequences, acting as foils against one another. Specifically, we apply multi-objective, two-population competitive coevolution (Luke 2013 ) to address our problem of finding optimal priority assignments (see Section  3 ). In our approach, the two populations of priority assignments and stress test scenarios, i.e., task-arrival sequences, evolve synchronously, competing with each other in order to search for optimal priority assignments that maximize the magnitude of safety margins from deadlines and the extent of constraint satisfaction. Note that better priority assignments enable a system to achieve larger safety margins. Hence, those priority assignments have a higher chance to pass stress test scenarios. This impacts the stress test scenarios because they need to evolve as well, aiming at inducing deadline misses in the system.

Recall from Section  4 that most of the existing SBSE research relies on search algorithms using a single population (Chen et al. 2018 ; Abdessalem et al. 2020 ; Shin et al. 2020 ). However, such algorithms do not fit the problem of priority assignments targeted here. When (1) two competing traits between task arrivals and priority assignments are encoded together in an individual of a single population and (2) two contradicting fitness functions regarding safety margins and deadline misses, which are exact opposites, assess such individuals, the notion of Pareto optimality is not applicable. In that case, maximizing the magnitude of safety margins necessarily entails minimizing the magnitude of deadline misses. Hence, a single population-based search algorithm cannot make Pareto improvements that maximize safety margins (resp. deadline misses) while not minimizing deadline misses (resp. safety margins). Specifically, the dominance relation over such individuals does not exist because if an individual I is strictly better than another individual \(I^{\prime }\) in one fitness value, I is always worse than \(I^{\prime }\) in the other fitness value. Hence, we are not able to obtain equally viable solutions with respect to the contradicting objectives using such a method.

Figure  3 shows an overview of our proposed solution: O ptimal P riority A ssignment M ethod for real-time tasks (OPAM). OPAM requires as input task descriptions defined by engineers, which specify task characteristics and their relationships (see Section  3 ). Given such input task descriptions, the “find worst task arrivals’ and “find best priority assignments” steps aim at generating worst-case sequences of task arrivals and best-case priority assignments, respectively. A worst-case sequence of task arrivals means that the magnitude of deadline misses, i.e., the amounts of time from task deadlines to task completion times, is maximized when tasks arrive as defined in the sequence. Note that if there is no deadline miss, a task-arrival sequence is considered worst-case if tasks complete their executions as close to their deadlines as possible. In contrast, a priority assignment is best-case when the magnitude of safety margins is maximized. Beyond maximizing safety margins, the “find best priority assignments” step accounts for satisfying engineering constraints in assigning priorities to tasks. OPAM evolves two competing populations of task-arrival sequences and priority assignments synchronously generated from the two steps. OPAM then outputs a set of priority assignments that are Pareto optimal with regards to the magnitude of safety margins and the extent of satisfying constraints. Hence, OPAM allows engineers to perform domain-specific trade-off analysis among Pareto solutions and is useful in practice to support decision making with respect to their task design. For example, suppose engineers develop a weakly hard real-time systems (Bernat and Burns 2001 ) that can tolerate occasional deadline misses. In that case, engineers may consider a few deadline misses as less important (as long as their consequences are negligible) than the overall magnitude of safety margins in their trade-off analysis. Section  6 describes OPAM in detail.

An overview of our O ptimal P riority A ssignment M ethod for real-time systems (OPAM)

6 Competitive Coevolution

Figure  4 describes the OPAM algorithm for finding optimal priority assignments, which employs multi-objective, two-population competitive coevolution. The algorithm first randomly initializes two populations A and P for task-arrival sequences and priority assignments, respectively (lines 13–15). For A , OPAM randomly varies task arrivals of aperiodic tasks to create p s a task-arrival sequences, according to the input task descriptions D . Regarding P , OPAM randomly creates p s p priority assignments that may include one defined by engineers if available.

Multi-objective two-population competitive coevolution for finding optimal priority assignments

The two populations sequentially evolve during the allotted analysis budget (see line 17 in Figure  4 ). The best priority assignment is the one that makes tasks schedulable and maximizes the magnitude of safety margins, while satisfying engineering constraints for a given worst sequence of task arrivals. Hence, searching for the best priority assignments involves searching for the worst sequences of task arrivals. We create two populations A and P searching for the worst arrival sequences and the best priority assignments, respectively. The fitness values of task-arrival sequences in A are computed based on how well they challenge the priority assignments in P , i.e., maximizing the magnitude of deadline misses (line 20). Likewise, the priority assignments in P are evaluated based on how well they perform against the task-arrival sequences in A , i.e., maximizing the magnitude of safety margins while satisfying constraints (line 25). Once the two populations are assessed against each other, OPAM generates the next populations based on the computed fitness values (lines 21 and 26). OPAM tailors the breading mechanisms of steady-state genetic algorithms (GA) (Whitley and Kauth 1988 ) for A and NSGAII (Deb et al. 2002 ) for P .

OPAM uses two types of fitness functions, namely internal and external fitness evaluations, which play a different and complementary role as described below. The two internal fitness evaluations in lines 20 and 25 of the listing in Figure  4 aim at selecting individuals – task-arrival sequences and priority assignments – for breeding the next A and P populations. OPAM evaluates the external fitness for the P population of priority assignments to find a best Pareto front (lines 28–31). As shown in lines 20 and 25, the internal fitness values of individuals in A (resp. P ) are computed based on how they perform with respect to individuals in P (resp. A ). Hence, an individual’s internal fitness is assessed through interactions with competing individuals. For example, a priority assignment in the first generation may have acceptable fitness values regarding safety margins and constraint satisfaction with respect to the first generation of task-arrival sequences, which are likely far from worst-case sequences. However, priority assignment fitness may get worse in later generations as the task-arrival sequences evolve towards larger deadline misses. Thus, if OPAM simply monitors internal fitness, it cannot reliably detect coevolutionary progress as an individual’s internal fitness changes according to competing individuals. The problem of monitoring progress in coevolution has been observed in many studies (Ficici 2004 ; Popovici et al. 2012 ). To address it, OPAM computes external fitness values of priority assignments in P based on a set E of task-arrival sequences generated independently from the coevolution process. By doing so, OPAM can observe the monotonic improvement of external fitness for priority assignments. We note that, in general, if interactions between two competing populations are finite and any interaction can be examined with non-zero probability at any time, monotonicity guarantees that a coevolutionary algorithm converges to a solution (Popovici et al. 2012 ).

We note that our approach for evolving task-arrival sequences is based on past work (Briand et al. 2005 ), where a specific genetic algorithm configuration was proposed to find worst-case task-arrival sequences. One significant modification is that OPAM accounts for task relationships – resource-dependency and task triggering relationships – and a multi-core scheduling policy based on simulations to evaluate the magnitude of deadline misses.

Following standard practice (Ralph et al. 2020 ), the next sections describe OPAM in detail by defining the representations, the scheduler, the fitness functions, and the evolutionary algorithms for coevolving the task-arrival sequences and priority assignments. We then describe the external fitness evaluation of OPAM.

6.1 Representations

OPAM coevolves two populations of task-arrival sequences and priority assignments. A task-arrival sequence is defined by their inter-arrival time characteristics (see Section  3 ). A priority assignment is defined by a function that maps priorities to tasks.

Task-arrival sequences.

Given a set J of tasks to be scheduled, a feasible sequence of task arrivals is a set A of tuples ( j , a t k ( j )) where j ∈ J and a t k ( j ) is the k th arrival time of a task j . Thus, a solution A represents a valid sequence of task arrivals of J (see valid a t k ( j ) computation in Section  3 ). Let \(\mathbb {T} = [0, \mathbf {T}]\) be the time period during which a scheduler receives task arrivals. The size of A is equal to the number of task arrivals over the \(\mathbb {T}\) time period. Due to the varying inter-arrival times of aperiodic tasks (Section  3 ), the size of A will vary across different sequences.

Priority assignments.

Given a set J of tasks to be scheduled, a feasible priority assignment is a list \(\rightarrow {P}\) of priority p r ( j ) for each task j ∈ J . OPAM assigns a non-negative integer to a priority p r ( j ) of j such that priorities are comparable to one another. The size of \(\rightarrow {P}\) is equal to the number of tasks in J . Each task in J has a unique priority. Hence, a priority assignment \(\rightarrow {P}\) is a permutation of all tasks’ priorities. We note that these characteristics of priority assignments are common in many real-time analysis methods (Audsley 2001 ; Davis and Burns 2007 ; Zhao and Zeng 2017 ) and industrial systems (e.g., see our six industrial case study systems described in Section  7.2 ).

6.2 Simulation

OPAM relies on simulation for analyzing the schedulability of tasks in a scalable way. For instance, an inter-arrival time of a software update task in a satellite system is approximately at most three months. In such cases, conducting an analysis based on an actual scheduler is prohibitively expensive. Also, applying an exhaustive technique for schedulability analysis typically doesn’t scale to an industrial system (e.g., see our experiment results using a model checker described in Section  7.8 ). Instead, OPAM uses a real-time task scheduling simulator, named OPAMScheduler, which applies a scheduling policy, i.e., single-queue multi-core scheduling policy (Arpaci-Dusseau and Arpaci-Dusseau 2018 ), based on discrete simulation time events. Note that we chose the single-queue multi-core scheduling policy for OPAMScheduler since our case study systems (described in Section  7.2 ) rely on this policy.

OPAMScheduler takes as input a feasible task-arrival sequence A and a priority assignment \(\rightarrow {P}\) for scheduling a set J of tasks. It then outputs a schedule scenario as a set S of tuples ( j , a t k ( j ), e t k ( j )) where a t k ( j ) and e t k ( j ) are the k th arrival and end time values of a task j , respectively (see Section  3 ). For each task j , OPAMScheduler computes e t k ( j ) based on its WCET and scheduling policy while accounting for task relationships (see the \({dp}(j,j^{\prime })\) resource-dependency relationship and the \({tr}(j,j^{\prime })\) task triggering relationship in Section  3 ). To simulate the worst-case executions of tasks, OPAMScheduler assigns tasks’ WCETs to their execution times.

OPAMScheduler implements a single-queue multi-core scheduling policy (Arpaci-Dusseau and Arpaci-Dusseau 2018 ), which schedules a task j with explicit priority p r ( j ) and deadline d l ( j ). When tasks arrive, OPAMScheduler puts them into a single queue that contains tasks to be scheduled. At any simulation time, if there are tasks in the queue and multiple cores are available to execute tasks, OPAMScheduler first fetches a task j from the queue in which j has the highest priority p r ( j ). OPAMScheduler then allocates task j to any available core. Note that if task j shares a resource with a running task \(j^{\prime }\) in another core, i.e., the \({dp}(j,j^{\prime })\) resource-dependency relationship holds, j will be blocked until \(j^{\prime }\) releases the shared resource.

OPAMScheduler works under the assumption that context switching time is negligible, which is also a working assumption in many scheduling analysis methods (Liu and Layland 1973 ; Audsley 2001 ; Di Alesio et al. 2015 ). Note that the assumption is practically valid and useful at an early development step in the context of real-time analysis. For instance, our collaborating partner, LuxSpace, accounts for the waiting time of tasks due to context switching between tasks through adding some extra time to WCET estimates at the task design stage. Note that OPAM can be applied with any scheduling policy, including those that account for context switching time and multiple queues.

6.3 Fitness functions

Internal fitness: deadline misses..

Given a feasible task-arrival sequence A and a priority assignment \(\rightarrow {P}\) , we formulate a function, \({fd}(A,\rightarrow {P})\) , to quantify the degree of deadline misses regarding a set J of tasks to be scheduled. To compute \({fd}(A,\rightarrow {P})\) , OPAM runs OPAMScheduler for A and \(\rightarrow {P}\) and obtains a schedule scenario S . We denote by d i s t k ( j ) the distance between the end time and the deadline of the k th arrival of task j observed in S and define d i s t k ( j ) = e t k ( j ) − a t k ( j ) + d l ( j ) (see Section  3 for the notation end time e t k ( a ), arrival time a t k ( j ), and deadline d l ( j )). We denote by l k ( j ) the last arrival index of a task j in A . Given a set J of tasks to be scheduled, the \({fd}(A,\rightarrow {P})\) function is defined as follows:

Note that \({fd}(A,\rightarrow {P})\) is defined as an exponential equation. Hence, when all task executions observed in a schedule scenario S meet their deadlines, \({fd}(A,\rightarrow {P})\) is a small value as any distance d i s t k ( j ) between the task end time and the deadline of the k th arrival of task j is a negative value. In contrast, deadline misses result in positive values for d i s t k ( j ). In such cases, \({fd}(A,\rightarrow {P})\) is a large value. The exponential form of \({fd}(A,\rightarrow {P})\) was precisely selected for this reason, to assign large values for deadline misses but small values when deadlines are met. By doing so, \({fd}(A,\rightarrow {P})\) prevents an undesirable solution that would result into many task executions meeting deadlines obfuscating a smaller number of deadline misses.

Following the principles of competitive coevolution, individuals in a population A of task-arrival sequences need to be assessed by pitting them against individuals in the other population P of priority assignments. We denote by f d ( A , P ) the internal fitness function that quantifies the overall magnitude of deadline misses across all priority assignment \(\rightarrow {P} \in \mathbf {P}\) , regarding a set J of tasks to be scheduled. The f d ( A , P ) fitness is used for breeding the next population of task-arrival sequences. OPAM aims to maximize f d ( A , P ), defined as follows:

Internal fitness: safety margins.

Given a feasible priority assignment \(\rightarrow {P}\) and a task-arrival sequence A , we denote by \({fs}(\rightarrow {P},A)\) the magnitude of safety margins regarding a set J of tasks to be scheduled. The computation of \({fs}(\rightarrow {P},A)\) is similar to the computation of \({fd}(A,\rightarrow {P})\) regarding the use of OPAMScheduler, which outputs a schedule scenario S . The difference is that OPAM reverses the sign of \({fd}(A,\rightarrow {P})\) as OPAM aims at maximizing the magnitude of safety margins. Given a set J of tasks to be scheduled, the \({fs}(\rightarrow {P},A)\) function is defined as follows:

Given two populations P and A of priority assignments and task-arrival sequences, similar to internal fitness f d ( A , P ), priority assignments in P need to be assessed against task-arrival sequences in A . We formulate an internal fitness function, \({fs}(\rightarrow {P},\mathbf {A})\) , to quantify the overall magnitude of safety margins across all task-arrival sequences A ∈ A , regarding a set J of tasks to be scheduled and a priority assignment \(\rightarrow {P}\) . OPAM relies on the \({fs}(\rightarrow {P},\mathbf {A})\) function to breed the next population of priority assignments. OPAM aims to maximize \({fs}(\rightarrow {P},\mathbf {A})\) , which is defined as follows:

Internal fitness: constraints.

Given a priority assignment \(\rightarrow {P}\) , we formulate an internal fitness function, \({fc}(\rightarrow {P})\) , to quantify the degree of satisfaction of soft constraints set by engineers. Such function is required as we recast the satisfaction of such constraints into an optimization problem, in order to minimize constraint violations. Specifically, OPAM accounts for the following constraint: aperiodic tasks should have lower priorities than those of periodic tasks. Recall from Section  2 that engineers consider this constraint to be desirable. We denote by \({lp}(\rightarrow {P})\) the lowest priority of periodic tasks in \(\rightarrow {P}\) . For a set J of tasks to be scheduled, OPAM aims to maximize \({fc}(\rightarrow {P})\) , which is defined as follows:

Greater p r ( j ) values denote higher priorities. Given a priority assignment \(\rightarrow {P}\) , if p r ( j ) for an aperiodic task j is lower than the priority of any of the periodic tasks, \({lp}(\rightarrow {P}) - {pr}(j)\) is a positive value. OPAM measures the difference between priorities of aperiodic and periodic tasks. By doing so, \({fc}(\rightarrow {P})\) rewards aperiodic tasks that satisfy the above constraint and consistently penalizes those that violate it. Hence, OPAM aims at maximizing \({fc}(\rightarrow {P})\) .

External fitness: safety margins and constraints.

To examine the quality of priority assignments and monitor the progress of coevolution, OPAM takes as input a set E of task-arrival sequences created independently from the coevolution process. Given a set E of task-arrival sequences and a priority assignment \(\rightarrow {P}\) , OPAM utilizes \({fs}(\rightarrow {P},\mathbf {E})\) and \({fc}(\rightarrow {P})\) described above as external fitness functions for quantifying the magnitude of safety margins and the extent of constraint satisfaction, respectively. As E does not change over the coevolution process, \({fs}(\rightarrow {P},\mathbf {E})\) is used for evaluating a priority assignment \(\rightarrow {P}\) since it is not impacted by the evolution of task-arrival sequences. Hence, external fitness functions ensure that OPAM monitors the progress of coevolution in a stable manner. Given two populations P and A of priority assignments and task-arrival sequences, we recall that the f d ( A , P ) internal fitness function quantifies the overall magnitude of deadline misses across all priority assignments in P for the given sequence of task arrivals A . The \({fs}(\rightarrow {P},\mathbf {A})\) internal fitness function quantifies the overall magnitude of safety margins across all sequences of task arrivals in A for the given priority assignments \(\rightarrow {P}\) . Hence, the internal fitness of A (resp. \(\rightarrow {P}\) ) is assessed through interactions with competing individuals in P (resp. A ). Therefore, if OPAM relies only on the internal fitness functions, it cannot gauge the progress of coevolution in a stable manner as an individual’s internal fitness depends on competing individuals.

We note that soft deadline tasks also require to execute within reasonable execution time, i.e., (soft) deadline. As the above fitness functions return quantified degrees of deadline misses and safety margins, OPAM uses the same fitness functions for both soft and hard deadline tasks.

6.4 Evolution: Worst-case task arrivals

The algorithm in Figure  5 describes in detail the evolution of task-arrival sequences in lines 18–21 of the listing in Figure  4 . OPAM adapts a steady-state Genetic Algorithm (GA) (Luke 2013 ) for evolving task-arrival sequences. As shown in lines 8–14, OPAM first evaluates each task-arrival sequence in the A population against the P population of priority assignments. OPAM executes OPAMScheduler to obtain a schedule scenario S for a task-arrival sequence A i ∈ A and a priority assignment \(\rightarrow {P}_{l} \in \mathbf {P}\) (line 11). OPAM then computes the internal fitness f d ( A i , P ) capturing the magnitude of deadline misses (lines 12–14). We note that a steady-state GA iteratively breeds offspring, assess their fitness, and then reintroduce them into a population. However, OPAM computes internal fitness of all task-arrival sequences in A at every generation. This is because internal fitness is computed in relation to P , which is coevolving with A .

A steady-state GA-based algorithm for evolving task-arrival sequences

Breeding the next population is done by using the following genetic operators: (1) Selection: OPAM selects candidate task-arrival sequences using a tournament selection technique, with the tournament size equal to two which is the most common setting (Gendreau and Potvin 2010 ) (line 17 in Figure  5 ). (2) Crossover: Selected candidate task-arrival sequences serve as parents to create offspring using a crossover operation (line 18). (3) Mutation: The offspring are then mutated (line 19). Below, we describe our crossover and mutation operators. Crossover. A crossover operator is used to produce offspring by mixing traits of parent solutions. OPAM modifies the standard one-point crossover operator (Luke 2013 ) as two parent task-arrival sequences A p and A q may have different sizes, i.e., | A p |≠| A q |. Let J = { j 1 , j 2 ,…, j m } be a set of tasks to be scheduled. Our crossover operator first randomly selects an aperiodic task j r ∈ J . For all i ∈ [1, r ] and j i ∈ J , OPAM then swaps all j i arrivals between the two task-arrival sequences A p and A q . Since J is fixed for all solutions, OPAM can cross over two solutions that may have different sizes. Mutation operator OPAM uses a heuristic mutation algorithm. For a task-arrival sequence A , OPAM mutates the k th task arrival time a t k ( j ) of an aperiodic task j with a mutation probability. OPAM chooses a new arrival time value of a t k ( j ) based on the [ p m i n ( j ), p m a x ( j )] inter-arrival time range of j . If such a mutation of the k th arrival time of j does not affect the validity of the k + 1th arrival time of j , the mutation operation ends. Specifically, let d be a mutated value of a t k ( j ). In case a t k + 1 ( j ) ∈ [ d + p m i n ( j ), d + p m a x ( j )], OPAM returns the mutated A task-arrival sequence.

After mutating the k th arrival time a t k ( j ) of a task j in a solution A , if the k + 1th arrival becomes invalid, OPAM corrects the remaining arrivals of j . Let o and d be, respectively, the original and mutated k th arrival time of j . For all the arrivals of j after d , OPAM first updates their original arrival time values by adding the difference d − o . Let \(\mathbb {T} = [0,\mathbf {T}]\) be the scheduling period. OPAM then removes some arrivals of j if they are mutated to arrive after T or adds new arrivals of j while ensuring that all tasks arrive within \(\mathbb {T}\) .

As shown in lines 20–26 in Figure  5 , the internal fitness of the generated offspring is computed based on the P population. OPAM then updates the A population of task-arrival sequences by comparing the offspring and individuals in A (line 27).

We note that when a system is only composed of periodic tasks, OPAM will skip evolving for worst-case arrival sequences as arrivals of periodic tasks are deterministic (see Section  3 ). Nevertheless, OPAM will optimize priority assignments based on given arrivals of periodic tasks. When needed, OPAM can be easily extended to manipulate offset and period values for periodic tasks, in a way identical to how we currently handle inter-arrival times for aperiodic tasks.

6.5 Evolution: Best-case priority assignments

Figure  6 shows the evolution procedure of priority assignments, which refines lines 23–26 in Figure  4 . OPAM tailors the Non-dominated Sorting Genetic Algorithm version 2 (NSGAII) (Deb et al. 2002 ) to generate a non-dominating (equally viable) set of priority assignments, representing the best trade-offs found among the given internal fitness functions. This is referred to as a Pareto nondominated front (Knowles and Corne 2000 ), where the dominance relation over priority assignments is defined as follows: A priority assignment \(\rightarrow {P}\) dominates another priority assignment \(\rightarrow {P}^{\prime }\) if \(\rightarrow {P}\) is not worse than \(\rightarrow {P}^{\prime }\) in all fitness values, and \(\rightarrow {P}\) is strictly better than \(\rightarrow {P}^{\prime }\) in at least one fitness value. NSGAII has been applied to many multi-objective optimization problems (Langdon et al. 2010 ; Shin et al. 2018 ; Wang et al. 2020 ).

An NSGAII-based algorithm for evolving priority assignments

OPAM maintains a population P of priority assignments as an archive that contains the best priority assignments discovered during coevolution. Unlike a standard application of NSGAII, in our study, we need to reevaluate the internal fitness values for priority assignments in P at every generation as the internal fitness values are computed based on the A population of task-arrival sequences, which coevolves. As shown in lines 9–16 in Figure  6 , OPAM first computes the internal fitness functions that measure the magnitude of safety margins and the extent of constraint satisfaction. OPAM then sorts non-dominated Pareto fronts (line 19) and assigns crowding distance (line 20) to introduce diversity among non-dominated priority assignments (Deb et al. 2002 ).

For breeding the next population of priority assignments (line 21 in Figure  6 , OPAM applies the following standard genetic operators (Sivanandam and Deepa 2008 ) that have been applied to many similar problems (Islam et al. 2012 ; Marchetto et al. 2016 ; Shin et al. 2018 ): (1) Selection. OPAM uses a binary tournament selection based on non-domination ranking and crowding distance. The binary tournament selection has been used in the original implementation of NSGAII (Deb et al. 2002 ). (2) Crossover. OPAM applies a partially mapped crossover (PMX) (Goldberg and Lingle 1985 ). PMX ensures that the generated offspring are valid permutations of priorities. (3) Mutation. OPAM uses a permutation swap method for mutating a priority assignment. This mutation method interchanges two randomly-selected priorities in a priority assignment according to a given mutation probability.

For the generated population P α of priority assignments, OPAM computes the two internal fitness functions (lines 22–29 in Figure  6 ). OPAM then sorts non-dominated Pareto fronts for the union of the current P and next P α populations (line 30), assign crowding distance (line 31), and select the best archive by accounting for the computed non-domination ranking and crowding distance (line 32).

6.6 External fitness evaluation

Figure  7 shows an algorithm that computes the external fitness functions and finds the best Pareto front, which refines lines 28–31 in Figure  4 . To monitor the coevolution progress in a stable manner, OPAM takes as input a set E of task-arrival sequences that are generated independently from the coevolution process. We use an adaptive random search technique (Chen et al. 2010 ) to sample task-arrival sequences in order to create E . The adaptive random search extends the naive random search by maximizing the Euclidean distance between the sampled points such that it maximizes the diversity of task-arrival sequences in E .

An algorithm for evaluating external fitness and finding the best Pareto front

As shown in lines 9–16 in Figure  7 , OPAM computes the two external fitness values for each priority assignment in the P population based on a given set E of task-arrival sequences. OPAM then sorts non-dominated Pareto fronts for the union of the P population and the current best Pareto front (line 17), assigns crowding distance (line 18), and selects the best Pareto front by accounting for the computed non-domination ranking and crowding distance (line 32). OPAM adopts NSGAII in order to maximize the diversity of priority assignments in the best Pareto front.

7 Evaluation

This section describes our evaluation of OPAM through six industrial case studies from different domains and several synthetic subjects. Our full evaluation package is available online (Lee et al. 2021 ).

7.1 Research questions

How does OPAM perform compared with Random Search? For search-based solutions, this RQ is an important sanity check to ensure that success is not due to the search problem being easy (Arcuri and Briand 2014 ). Our conjecture is that a search-based algorithm, although expensive, will significantly outperform naive random search (RS).

Is competitive coevolution suitable to find best-case priority assignments? We conjecture that a coevolutionary algorithm is a suitable solution to address the priority assignment problem since it is solved, in practice, through a competing interactive process between the development and testing teams. To answer this RQ, we compare OPAM with a sequential approach that first looks for worst-case sequences of task arrivals and then tries to find best-case priority assignments.

Can OPAM find (near-)optimal solutions for large-scale systems in a reasonable time budget? In this RQ, we investigate the scalability of OPAM by conducting some experiments with systems of various sizes, including six industrial and several synthetic subjects. We study the relationship between OPAM’s performance measures and the characteristics of study subjects.

How do priority assignments generated by OPAM compare with priority assignments defined by engineers? OPAM can be considered useful only when it finds priority assignments that show benefits over those defined (manually) by engineers with domain expertise. This RQ therefore compares the quality of priority assignments generated by OPAM with those defined by engineers. We further discuss the usefulness of OPAM from a practical perspective, based on the feedback received from engineers in LuxSpace.

7.2 Industrial study subjects

To evaluate RQs in realistic and diverse settings, we apply OPAM to six industrial study subjects from different domains such as aerospace, automotive, and avionics domains. Specifically, we obtained one case study subject from our industry partner, LuxSpace. We found the other five industrial study subjects in the literature (Di Alesio et al. 2015 ), which, consistent with the LuxSpace system, all assume a single-queue, multi-core, fixed-priority scheduling policy. Note that OPAM uses the same scheduling policy (described in Section  6.2 ) as in Di Alesio et al.’s work. This policy uses fixed priorities that are determined offline and therefore do not change dynamically. Table  2 summarizes the relevant attributes of these subjects, presenting the number of periodic and aperiodic tasks, resource dependencies, triggering relations, and platform cores. The subjects are characterized by real-time parameters, e.g., periods, deadlines, and priorities, described in Section  3 . We note that all the study subjects are deadlock-free systems as they do not have circular resource dependencies. Regarding task priorities, all tasks in the six subjects have fixed priorities, which are defined by experts in their domains. The full task descriptions (including WCET, inter-arrival times, periods, deadlines, priorities, and relationship details) of the subjects are available online (Lee et al. 2021 ). The main missions of the six subjects are described as follows:

ICS is an ignition control system that checks the status of an automotive engine and corrects any errors of the engine (Peraldi-Frati and Sorel 2008 ). The system was developed by Bosch GmbH. Footnote 1

CCS is a cruise control system that acquires data from vehicle sensors and maintains the specified vehicle speed (Anssi et al. 2011 ). Continental AG Footnote 2 developed the system.

UAV is a mini unmanned air vehicle that follows dynamically defined way-points and communicates with a ground station to receive instructions (Traore et al. 2006 ). The system was developed in collaboration with the University of Poitiers France and ENSMA. Footnote 3

GAP is a generic avionics platform for a military aircraft (Locke et al. 1990 ). The system was designed in a joint project with Carnegie Mellon University, the US Navy, and IBM Footnote 4 , aiming at supporting several missions regarding air-to-surface attacks.

HPSS is a satellite system for two satellites, named Herschel and Planck (Mikučionis et al. 2010 ). The two satellites share the same computational architecture, although they have different scientific missions. Herschel aims at studying the origin and evolution of stars and galaxies. Planck’s primary mission is the study of the relic radiation from the Big Bang. ESA Footnote 5 carried out the HPSS project.

ESAIL is a microsatellite for tracking ships worldwide by detecting messages that ships radio-broadcast (see Section  2 ). Luxspace, our industry partner, developed ESAIL in an ESA project.

7.3 Synthetic study subjects

To investigate RQ3, we use synthetic subjects in order to freely control key parameters in real-time systems. We create a set of tasks by adopting a well-known procedure (Emberson et al. 2010 ) for synthesizing real-time tasks, which has been applied in many schedulability analysis studies (Davis et al. 2008 ; Zhang and Burns 2009 ; Davis and Burns 2011 ; Grass and Nguyen 2018 ; Du̇rr et al. 2019 ).

Figure  8 describes a procedure that synthesizes a set of real-time tasks. For a given number n of tasks and a target utilization u t , the procedure first generates a set U of task utilization values by using the UUniFast-Discard algorithm (Davis and Burns 2011 ) (line 13). The UUniFast-Discard algorithm is devised to give an unbiased distribution of utilization values, where a utilization U j ∈ U is a positive value and \({\sum }_{U_{j} \in \mathbf {U}} U_{j} = u_{t}\) .

An algorithm for synthesizing a set of tasks

The procedure then generates a set I of n task periods according to a log-uniform distribution within a range [ p d m i n , p d m a x ], i.e., given a task period (random variable) I j ∈ I , \(\log {I_{j}}\) follows a uniform distribution (line 14 in Figure  8 ). For example, when the minimum and maximum task periods are p d m i n = 10ms and p d m a x = 1000ms, respectively, the procedure generates (approximately) an equal number of tasks in time intervals [10ms, 100ms] and [100ms, 1000ms]. The parameter g is used to choose the granularity of the periods, i.e., task periods are multiples of g . Such a distribution of task periods provides a reasonable degree of realism with respect to what is usually observed in real systems (Baruah et al. 2011 ).

As shown in lines 15–16 of the procedure in Figure  8 , a set C of task WCETs are computed based on the set U of task utilization values and the set I of task periods. Specifically, a task WCET C j ∈ C is computed as C j = U j ⋅ I j .

As per line 17 of the listing in Figure  8 , the procedure synthesizes a set S of tasks. A task j is characterized by a period I j and a WCET C j and it is associated with a deadline d l ( j ) and a priority p r ( j ). According to the rate-monotonic scheduling policy (Liu and Layland 1973 ), tasks’ deadlines are equal to their periods and tasks with shorter periods are given higher priorities.

To synthesize aperiodic tasks, the procedure converts some periodic tasks to aperiodic tasks according to a given ratio γ of aperiodic tasks among all tasks (see line 19 in Figure  8 ). A range factor μ is used to determine maximum inter-arrival times of aperiodic tasks. Specifically, for a task j to be converted, the procedure sets the minimum inter-arrival time p m i n ( j ) as p m i n ( j ) = I j . The procedure then selects a uniformly distributed value x from the range (1, μ ] and computes the maximum inter-arrival time p m a x ( j ) as p m a x ( j ) = x ⋅ I j .

7.4 Experimental Design

This section describes how we design experiments to answer the RQs described in Section  7.1 . We conducted four experiments, EXP1, EXP2, EXP3, and EXP4, as described below.

To answer RQ1, EXP1 compares OPAM with our baseline, which relies on random search, to ensure that the effectiveness of OPAM is not due to the search problem being simple. Our baseline, named RS, replaces GA with a random search for finding worst-case sequences of task arrivals and NSGAII with a random search for finding best-case priority assignments. Note that RS uses the same internal and external fitness functions (see Section  6.3 ) and also maintains the best populations during search; however, it does not employ any genetic operators, i.e., crossover and mutation. In EXP1, we applied OPAM and RS to the six industrial subjects described in Section  7.2 .

Recall from Section  6.3 that OPAM uses a set E of task-arrival sequences that are generated independently from the coevolution process in order to monitor the coevolution progress in a stable manner. As OPAM and RS use the same set E of task-arrival sequences, EXP1 first compares OPAM and RS based on E . In addition, EXP1 examines how well the solutions, i.e., priority assignments, found by OPAM and RS perform with other sequences of task arrivals. To do so, we create six sets of sequences of task arrivals for each study subject by varying the method to generate task-arrival sequences and the number of task-arrival sequences. Note that task-arrival sequences generated by different methods are valid with respect to the inter-arrival times defined in each study subject. Below we describe the six sets of task-arrival sequences generated for each subject.

\(\mathbf {T}_{a}^{10}\) : A set of task-arrival sequences generated by using an adaptive random search technique (Chen et al. 2010 ) that aims at maximizing the diversity of task-arrival sequences. The \(\mathbf {T}_{a}^{10}\) set contains 10 sequences of task arrivals.

\(\mathbf {T}_{w}^{10}\) : A set of task-arrival sequences generated by using a stress test case generation method that aims at maximizing the chances of deadline misses in task executions. The stress test case generation method extends prior work (Briand et al. 2005 ). The extended method uses the fitness function regarding deadline misses and genetic operators that OPAM introduces for evolving worst-case task-arrival sequences (see Section  6 ). The \(\mathbf {T}_{w}^{10}\) set contains 10 sequences of task arrivals.

\(\mathbf {T}_{r}^{10}\) : A set of task-arrival sequences generated randomly. The \(\mathbf {T}_{r}^{10}\) set has 10 sequences of task arrivals.

\(\mathbf {T}_{a}^{500}\) : A set of task-arrival sequences generated by using the adaptive random search technique. The \(\mathbf {T}_{a}^{500}\) set contains 500 sequences of task arrivals.

\(\mathbf {T}_{w}^{500}\) : A set of task-arrival sequences generated by using the stress test case generation method. The \(\mathbf {T}_{w}^{500}\) set contains 500 sequences of task arrivals.

\(\mathbf {T}_{r}^{500}\) : A set of task-arrival sequences generated randomly. The \(\mathbf {T}_{r}^{500}\) set has 500 sequences of task arrivals.

To answer RQ2, EXP2 compares OPAM with a priority assignment method, named SEQ, that relies on one-population search algorithms. SEQ first finds a set of worst-case sequences of task arrivals using GA with the fitness function that measures the magnitude of deadline misses (see f d () in Section  6.3 ) and the genetic operators described in Section  6.4 . Given a set of worst-case task-arrival sequences obtained from GA, SEQ then aims at finding best-case priority assignments using NSGAII with the fitness functions that quantify the magnitude of safety margins and the degree of constraint satisfaction (see f s () and f c (), respectively, in Section  6.3 ) and the genetic operators described in Section  6.5 .

We note that SEQ does not use the external fitness functions as it does not coevolve task-arrival sequences and priority assignments. Hence, the numbers of fitness evaluations of the two methods are not comparable. To fairly compare OPAM and SEQ, we set the same time budget for the two methods. Specifically, we first measure the execution time of OPAM for analyzing each subject. We then split the execution time in half and set each half time as the execution budget of the GA and NSGAII steps in SEQ for the corresponding subject. In order to assess the quality of priority assignments obtained from OPAM and SEQ, we use the sets of task-arrival sequences described in EXP1, i.e., \(\mathbf {T}_{a}^{10}\) , \(\mathbf {T}_{w}^{10}\) , \(\mathbf {T}_{r}^{10}\) , \(\mathbf {T}_{a}^{500}\) , \(\mathbf {T}_{w}^{500}\) , and \(\mathbf {T}_{r}^{500}\) , which are created independently from the two methods.

To answer RQ3, EXP3 examines not only the six industrial subjects but also 370 synthetic subjects. We create the synthetic subjects to study correlations between the execution time and memory usage of OPAM and the following parameters: the number of tasks ( n ), a (part-to-whole) ratio of aperiodic tasks ( γ ), a range factor for maximum inter-arrival times ( μ ), and simulation time ( T ), as described in Sections  7.3 and  6 . We note that we chose to control parameters n , γ , and μ because they are the main parameters on which engineers have control to define tasks in real-time systems. Simulation time T obviously impacts the execution time of OPAM as well. But EXP3 aims at modeling such correlations precisely and providing experimental results. Regarding the other factors that define, for example, task relationships and platform cores, we note significant diversity across the six industrial subjects.

Recall from Section  7.3 that we use the task generation procedure presented in Figure  8 to synthesize tasks. For EXP3, we set some parameter values of the procedure as follows: (1) Target utilization u t = 0.7, which is a common objective in the development of a real-time system in order to guarantee the schedulability of tasks (Fineberg and Serlin 1967 ; Du̇rr et al. 2019 ). (2) The range of task periods [ p d m i n , p d m a x ] = [10ms,1s], which are common values in many real-time systems (Emberson et al. 2010 ; Baruah et al. 2011 ). (3) The granularity of task periods g = 10ms in order to increase realism as most of the task periods in our industrial subjects are multiples of 10ms. Because of some degree of randomness in the procedure of Figure  8 , we create ten synthetic subjects per configuration. Below we further describe how synthetic subjects are created for each controlled experiment. EXP3.1. To study the correlations between the execution time and memory usage of OPAM with the number of tasks n , we create nine sets of ten synthetic subjects such that no two sets have the same number of tasks. Specifically, we create sets with 10, 15, ..., 50 tasks, respectively. Regarding the ratio of aperiodic tasks, γ = 0.4 as, on average, the ratio of aperiodic tasks to periodic tasks in our industrial subjects is 2/3. For the range factor, μ = 2, which is determined based on the inter-arrival times of aperiodic tasks in our industry subjects. We set the simulation time T to 2s in order to ensure that any aperiodic task arrives at least once during that time. We note that, given the maximum task period p d m a x = 1s and the range factor μ = 2, the maximum inter-arrival time of an aperiodic task is at most 2s (see Section  7.3 ). EXP3.2. To study the correlations between the execution time and memory usage of OPAM with the ratio of aperiodic tasks γ , we create ten sets of synthetic subjects by setting this ratio to the following values: 0.05, 0.10, ..., 0.50. We set the number of tasks to 20 ( n = 20), which is the average number of tasks in our six industrial subjects. Regarding the other parameters, range factor and simulation time, μ = 2 and T = 2s are set as discussed in EXP3.1. EXP3.3. To study the correlations between the execution time and memory usage of OPAM with the range factor μ that is used to determine the maximum inter-arrival times, we create nine sets of synthetic subjects by setting μ to 2, 3, ..., 10. We set the simulation time as follows: T = 10s. This ensures that any aperiodic task arrives at least once during the simulation time when μ is at most 10 (see Section  7.3 ). The other parameters, the number of tasks and ratio of aperiodic tasks, n = 20 and γ = 0.4 are set as discussed in EXP3.1 and EXP3.2. EXP3.4. To study the correlations between the execution time and memory usage of OPAM with the simulation time T , we create nine sets of synthetic subjects by setting T to 2s, 3s, ..., 10s. The other parameters, e.g., the number of tasks, the ratio of aperiodic tasks, and the range factor, n = 20, γ = 0.4, and μ = 2, are set as discussed in EXP3.1 and EXP3.2.

To answer RQ4, EXP4 compares priority assignments optimized by OPAM and those defined by engineers. We apply OPAM to the six industrial subjects (see Section  7.2 ) which include priority assignments defined by practitioners. Note that we focus here on the ESAIL subject in collaboration with our industry partner, LuxSpace; The other five subjects are from the literature (Di Alesio et al. 2015 ) and hence we can only collect feedback from practitioners for ESAIL.

7.5 Evaluation metrics

Multi-objective evaluation metrics..

In order to fairly compare the results of search algorithms, based on existing guidelines (Li et al. 2020 ) for assessing multi-objective search algorithms, we use complementary quality indicators: Hypervolume (HV) (Zitzler and Thiele 1999 ), Pareto Compliant Generational Distance (GD+) (Ishibuchi et al. 2015 ), and Spread (Δ) (Deb et al. 2002 ). To compute the GD+ and Δ quality indicators, following the usual procedure (Li et al. 2020 ), we create a reference Pareto front as the union of all the non-dominated solutions obtained from all runs of the algorithms being compared. Identifying the optimal (ideal) Pareto front is typically infeasible for a complex optimization problem (Li et al. 2020 ). Key features of the three quality indicators are described below.

HV is defined to measure the volume in the objective space that is covered by members of a Pareto front generated by a search algorithm (Zitzler and Thiele 1999 ). The higher the HV values, the more optimal the search outputs.

GD+ is defined to measure the distance between the points on a Pareto front obtained from a search algorithm and the nearest points on a reference Pareto front (Ishibuchi et al. 2015 ). GD+ modifies General Distance (GD) (Veldhuizen and Lamont 1998 ) to account for the dominance relations when computing the distances. The lower the GD+ values, the more optimal the search outputs.

Δ is defined to measure the extent of spread among the points on a Pareto front computed by a search algorithm (Deb et al. 2002 ). We note that OPAM aims at obtaining a wide variety of equally-viable priority assignments on a Pareto front (see Section  6 ). The lower the Spread values, the more spread out the search outputs.

Interpretable metrics.

The two external fitness functions described in Section  6 mainly aim at effectively guiding search. It is, however, difficult for practitioners to interpret the computed fitness values. Since they are not intuitive to practitioners, to assess the usefulness of OPAM from a practitioner perspective, we measure (1) the safety margins from tasks’ completion times to their deadlines across our experiments and (2) the number of constraint violations in a priority assignment. In addition, we measure the execution time and memory usage of OPAM.

Statistical comparison metrics.

To statistically compare our experiment results, we use the Mann-Whitney U-test (Mann and Whitney 1947 ) and Vargha and Delaney’s \(\hat {A}_{12}\) effect size (Vargha and Delaney 2000 ), which have been frequently applied for evaluating search-based algorithms (Arcuri et al. 2010 ; Hemmati et al. 2013 ; Shin et al. 2018 ). Mann-Whitney U-test determines whether two independent samples are likely or not to belong to the same distribution. We set the level of significance, α , to 0.05. Vargha and Delaney’s \(\hat {A}_{12}\) measures probabilistic superiority – effect size – between search algorithms. Two algorithms are considered to be equivalent when the value of \(\hat {A}_{12}\) is 0.5.

7.6 Parameter tuning and implementation

Parameters for coevolutionary search..

For the coevolutionary search parameters, we set the population size to 10, the crossover rate to 0.8, and the mutation rate to 1/| J |, where | J | denotes the number of tasks. We apply these parameter values for both the evolution of task-arrival sequences and priority assignments (see Section  6 ). These values are determined based on existing guidelines (Arcuri and Fraser 2011 ; Sayyad et al. 2013 ) and previous work (Lee et al. 2020b ).

We determine the number of coevolution cycles (see Section  6 ) based on an initial experiment. We applied OPAM to the six industrial subjects and ran OPAM 50 times for each subject. From the experiment results, we observed that there is no notable difference in Pareto fronts generated after 1000 cycles. Hence, we set the number of coevolution cycles to 1000 in our experiments, i.e., EXP1, EXP2, and EXP3 described in Section  7.4 .

Parameters for evaluating fitness functions.

To evaluate external fitness functions, we use a set of task-arrival sequences that are generated independently from the coevolution process (see Section  6.6 ). We use an adaptive random search (Chen et al. 2010 ) to generate a set E of task-arrival sequences, which varies task arrival times within the specified inter-arrival time ranges of aperiodic tasks. We set the size of E to 10. From our initial experiment, we observed that this is sufficient to compute the external fitness functions of OPAM under a reasonable time, i.e., less than 15s. We note that E contains two default sequences of task arrivals as follows: (seq. 1) aperiodic tasks always arrive at their maximum inter-arrival times and (seq. 2) aperiodic tasks always arrive at their minimum inter-arrival times. By having those two sequences of task arrivals as initial elements in E , the adaptive random search finds other sequences of task arrivals to maximize the diversity of elements in E .

If a system contains only periodic tasks, the simulation time is often set as the least common multiple (LCM) of their periods to account for all possible arrivals (Peng et al. 1997 ). However, as the six industrial subjects include aperiodic tasks, this is not applicable. For the experiments with the six industrial subjects, we set the simulation time to the maximum time between the LCM of periodic tasks’ periods and the maximum inter-arrival time among aperiodic tasks. By doing so, all possible arrival patterns of periodic tasks are examined and any aperiodic task arrives at least once during simulation. Recall from Section  6.4 that OPAM varies arrival times of aperiodic tasks to find worst-case sequences of task arrivals.

We note that the parameters mentioned above can probably be further tuned to improve the performance of our approach. However, since with our current setting, we were able to convincingly and clearly support our conclusions, we do not report further experiments on tuning those values.

Implementation.

We implemented OPAM by extending jMetal (Durillo and Nebro 2011 ), which is a metaheuristic optimization framework supporting NSGAII and GA. We conducted our experiments using the high-performance computing cluster (Varrette et al. 2014 ) at the University of Luxembourg. To account for randomness, we repeated each run of OPAM 50 times for all experiments. Each run of OPAM was executed on a different node (equipped with five 2.5GHz cores and 20GB memory) of the cluster, and took less than 16 hours.

7.7 Results

Figure  9 shows the best Pareto fronts obtained with 50 runs of OPAM and RS, for the six industrial study subjects described in Section  7.2 . The fitness values presented in the figures are computed based on each subject’s set E of task-arrival sequences (see Section  7.6 ), which is created independently from OPAM and RS. Figures  9a , c, d, e, and d indicate that OPAM finds significantly better solutions than RS for ICS, UAV, GAP, HPSS, and ESAIL. Regarding CCS (see Figure  9b ), it is difficult to conclude anything based only on visual inspection. Hence, we compared Pareto fronts obtained by OPAM and RS using the three quality indicators HV, GD+, and Δ, described in Section  7.5 .

Pareto fronts obtained by OPAM and RS for the six industrial subjects: (a) ICS, (b) CCS, (c) UAV, (d) GAP, (e) HPSS, and (f) ESAIL. The fitness values are computed based on each subject’s set E of task-arrival sequences (see Section  7.6 ). The points located closer to the bottom left of each plot are considered to be better priority assignments when compared to points closer to the top right

Figure  10 depicts distributions of HV (Figure  10a ), GD+ (Figure  10b ), and Δ (Figure  10c ) for the six industrial subjects. The boxplots in the figures present the distributions (25%-50%-75%) of the quality values obtained from 50 runs of OPAM and RS. The quality values are computed based on the Pareto fronts obtained by the algorithms and each subject’s set E of task-arrival sequences (see Section  7.6 ). In the figures, statistical comparisons of the two corresponding distributions are summarized using p-values and \(\hat {A}_{12}\) values, as described in Section  7.5 , under each subject name.

Comparing OPAM and RS using the three quality indicators: (a) HV, (b) GD+, and (c) Δ. The boxplots (25%-50%-75%) show the quality values obtained from 50 runs of OPAM and RS. The quality values are computed based on the Pareto fronts obtained by the algorithms and each subject’s set E of task-arrival sequences (see Section  7.6 )

As shown in Fig.  10a and b, OPAM obtains better distributions of HV and GD+ compared to RS for all six subjects. All the differences are statistically significant as the p-values are below 0.05. Regarding Δ, as depicted in Fig.  10c , OPAM yields higher diversity in Pareto front solutions than RS for the following subjects: UAV, GAP, and HPSS. For ICS, CCS, and ESAIL, OPAM and RS obtain similar Δ values. From Fig.  10a and b, and Table  2 , we also observe that the higher the number of aperiodic tasks in a subject, the larger the differences in HV and GD+ between OPAM and RS. Hence, for these two quality indicators, OPAM outperforms RS more significantly for more complex search problems. Note that the number of aperiodic tasks is one of the main factors that drives the degree of uncertainty in task arrivals.

Given the Pareto priority assignments obtained by OPAM and RS, we further assessed the quality values of the solutions by evaluating them with different sets of task-arrival sequences. As described in Section  7.4 , we created six test sets of task-arrival sequences for each subject by varying the sequence generation methods and the number of task-arrival sequences in a set (see \(\mathbf {T}_{a}^{10}\) , \(\mathbf {T}_{w}^{10}\) , \(\mathbf {T}_{r}^{10}\) , \(\mathbf {T}_{a}^{500}\) , \(\mathbf {T}_{w}^{500}\) , and \(\mathbf {T}_{r}^{500}\) described in Section  7.4 ). Table  3 reports the average quality values measured by HV, GD+, and Δ based on 50 runs of OPAM and RS with the different test sets of task-arrival sequences. The results indicate that OPAM significantly outperforms RS in most comparison cases. Specifically, out of a total of 108 comparisons, OPAM outperforms RS 87 times (see the blue-colored cells related to OPAM in Table  3 ). Regarding Δ, RS outperforms OPAM for the CCS subject (see the gray-colored cells related to RS in Table  3 ). As shown in Table  2 , CCS has only 3 aperiodic tasks and RS was therefore able to find better solutions with respect to Δ for such a simple subject.

To compare OPAM and SEQ, we first visually inspect the best Pareto fronts obtained from 50 runs of OPAM and SEQ for the six study systems described in Section  7.2 by varying the test sets of task-arrival sequences for each subject (see \(\mathbf {T}_{a}^{10}\) , \(\mathbf {T}_{w}^{10}\) , \(\mathbf {T}_{r}^{10}\) , \(\mathbf {T}_{a}^{500}\) , \(\mathbf {T}_{w}^{500}\) , and \(\mathbf {T}_{r}^{500}\) described in Section  7.4 ), which are created independently from OPAM and SEQ. Overall, we observed that OPAM finds significantly better priority assignments in most cases. For example, Figure  11 depicts the best Pareto fronts obtained by OPAM and SEQ when the fitness values are computed based on each subject’s test set \(\mathbf {T}_{a}^{500}\) of 500 task-arrival sequences, which are generated with adaptive random search. The results clearly show that OPAM outperforms SEQ with respect to producing more optimal Pareto fronts for ICS, CCS, UAV, HPSS, and ESAIL. For GAP, the visual inspection is not sufficient to provide any conclusions. Hence, we further compare OPAM and SEQ based on the quality indicators described in Section  7.5 .

Pareto fronts obtained by OPAM and SEQ for the six industrial subjects: (a) ICS, (b) CCS, (c) UAV, (d) GAP, (e) HPSS, and (f) ESAIL. The fitness values are computed based on each subject’s set \(\mathbf {T}_{a}^{500}\) of task-arrival sequences (see Section  7.4 ). The points located closer to the bottom left of each plot are considered to be better priority assignments when compared to points closer to the top right

Table  4 compares the quality values measured by HV, GD+, and Δ for the six study subjects. To fairly compare the priority assignments obtained by OPAM and SEQ, we assess them with the test sets of task-arrival sequences for each subject (see \(\mathbf {T}_{a}^{10}\) , \(\mathbf {T}_{w}^{10}\) , \(\mathbf {T}_{r}^{10}\) , \(\mathbf {T}_{a}^{500}\) , \(\mathbf {T}_{w}^{500}\) , and \(\mathbf {T}_{r}^{500}\) described in Section  7.4 ). Table  4 reports the average quality values computed based on 50 runs of OPAM and SEQ. In Table  4 , the statistical comparison of the two corresponding distributions are reported using p-values and \(\hat {A}_{12}\) values.

As shown in Table  4 , we compared OPAM and SEQ 108 times by varying the study subjects, the quality indicators, the number of task-arrival sequences, and the task-arrival sequence generation methods. Out of 108 comparisons, OPAM significantly outperforms SEQ 63 times. Specifically, out of 36 HV comparisons, OPAM obtains better HV values than SEQ 28 times. For ICS (6 HV comparisons), the differences in HV values between OPAM and SEQ are not statistically significant. In only one HV comparison for CCS, SEQ outperforms OPAM (see the gray-colored cell related to HV and CCS in Table  4 ). To interpret these results, one must recall from Table  2 that ICS and CCS have only three aperiodic tasks that impact the degree of uncertainty in task arrivals and therefore represent simple cases. Out of 36 GD+ comparisons, OPAM outperforms SEQ 32 times. SEQ outperforms OPAM only two times for CCS. Hence, overall, the results indicate that OPAM outperforms SEQ, in terms of generating more optimal Pareto fronts, when the subjects feature a considerable degree of uncertainty in task arrivals and therefore make our search problem more complex. Otherwise differences are not statistically or practically significant. Regarding Δ, which focuses on the diversity of solutions on the Pareto front, SEQ outperforms OPAM 24 times out of 36 comparisons (see the gray-colored cells related to Δ in Table  4 ). However, since OPAM produces enough alternative priority assignments spreading across Pareto fronts (as visible from the solutions obtained by OPAM in Figure  11 ), these differences in Δ have limited implications in practice.

Table  5 reports the average execution times and memory usage required to run OPAM for the six industrial subjects, over 50 runs. As shown in Table  5 , finding optimal priority assignments for ESAIL requires the largest execution time (≈ 15.5h) and memory usage (≈ 2.9GB), compared to the other subjects. We note that such execution time and memory usage are acceptable as OPAM can be executed offline in practice.

Figures  12 and  13 show, respectively, the execution times and memory usage from EXP3.1 (a), EXP3.2 (b), EXP3.3 (c), and EXP3.4 (d), described in Section  7.4 . The boxplots in the figures show distributions (25%-50%-75%) obtained from 50 × 10 runs of OPAM for a set of 10 synthetic subjects, which are created with the same experimental setting. Regarding the execution time of OPAM, Figures  12a and d show that the execution time of OPAM is linear both in the number of tasks and simulation time. As for the memory usage of OPAM, results in Figures  13a and d indicate that memory usage is linear both in the number of tasks and in the simulation time. However, the results depicted in Figures  12b , c,  13b , and c indicate that there are no correlations between OPAM execution time and memory usage and the following two parameters: ratio of aperiodic tasks and range factor. Therefore, we expect OPAM to scale well as the numbers of tasks and simulation time increase.

Execution times of OPAM when varying the values of the following parameters: (a) number of tasks n , (b) ratio of aperiodic tasks γ , (c) range factor μ , and (d) simulation time T . The boxplots (25%-50%-75%) show the execution times obtained from 500 runs of OPAM, i.e., 50 runs for each of the 10 synthetic subjects with the same configuration

Memory usage of OPAM when varying the values of the following parameters: (a) number of tasks n , (b) ratio of aperiodic tasks γ , (c) range factor μ , and (d) simulation time T . The boxplots (25%-50%-75%) show the memory usage obtained from 500 runs of OPAM, i.e., 50 runs for each of the synthetic subjects with the same configuration

Figure  14 compares, with respect to external fitness (see the f s () and f c () fitness functions and the set E of sequences of task arrivals described in Section  6.6 ), the Pareto solutions obtained by OPAM against the priority assignments defined by engineers for the six industrial subjects: ICS (Figure  14a ), CCS (Figure  14b ), UAV (Figure  14c ), GAP (Figure  14d ), HPSS (Figure  14e ), and ESAIL (Figure  14f ).

Comparing Pareto solutions obtained by OPAM and priority assignments defined by engineers for the six industrial subjects: (a) ICS, (b) CCS, (c) UAV, (d) GAP, (e) HPSS, and (f) ESAIL. The points located closer to the bottom left of each plot are considered to be better priority assignments when compared to points closer to the top right

As shown in the figure, the solutions obtained by OPAM clearly outperform the priority assignments defined by engineers regarding the two external objectives: the magnitude of safety margins and the extent to which constraints are satisfied.

Table  6 summarizes safety margins from the task executions of ESAIL when using one of our priority assignments optimized by OPAM and the one defined by engineers at LuxSpace. Note that we focus on ESAIL as it is not possible to access the engineers who developed the other five industrial subjects reported in the literature (Locke et al. 1990 ; Traore et al. 2006 ; Peraldi-Frati and Sorel 2008 ; Mikučionis et al. 2010 ; Anssi et al. 2011 ). For comparison, we chose the bottom-left solution in Fig.  14f since it is optimal for the constraint fitness, which is the same as the fitness value of the priority assignment defined by engineers, and the differences in safety margin fitness among our solutions are negligible.

As shown in Table  6 , our optimized priority assignment significantly outperforms the one of engineers. Our solution increases safety margins, on average, by 5.33% compared to the engineers’ solution. For aperiodic tasks, our solution decreases safety margins by 0.01% (4.2ms difference) when the safety margins being compared are the maximum margins observed in both solutions (see the maximum safety margins, 59710.3ms obtained by engineers’ solution and 59707.2ms obtained by OPAM, in Table  6 ). Such a small decrease is however negligible in the context of ESAIL as the maximum safety margin obtained by our solution is still large, i.e., ≈ 1m. For periodic tasks, we note that our solution increases safety margins by 208.09% when the safety margins being compared are the minimum margins observed in both solutions (see the minimum safety margins, -44.5ms obtained by engineers’ solution and 48.1ms obtained by OPAM, in Table  6 ). Note that the minimum safety margin of -44.5ms obtained with the engineers’ solution indicates that a task violates its deadline. In the context of ESAIL, which is a mission-critical system, such gain in safety margins in the executions of periodic tasks is important because the hard deadlines of periodic tasks are more critical than the soft deadlines of aperiodic tasks.

Investigating practitioners’ perceptions of the benefits of OPAM is necessary to adopt OPAM in practice. To do so, we draw on the qualitative reflections of three software engineers at LuxSpace, with whom we have been collaborating on this research. They have had four to seven years of experience developing satellite systems at LuxSpace, with more than 50 years of collective experience in companies. All the reflections are based on observations made throughout our interactions. The engineers at LuxSpace deemed OPAM to be an improvement over their current practice as it allows them to perform domain-specific trade-off analysis among Pareto solutions and is useful in practice to support decision making with respect to their task design. Encouraged by the promising results, we are now applying OPAM to new systems in collaboration with LuxSpace.

7.8 Threats to Validity

To mitigate the main threats that arise from not accounting for random variation, we compared OPAM against RS under identical parameter settings. We present all the underlying parameters and provide the full package of our experiments to facilitate replication. Also, we ran OPAM 50 times for each study subject and compared results using statistical analysis, i.e., Mann-Whitney U-test and Vargha and Delaney’s \(\hat {A}_{12}\) .

We note that there are prior studies that aim at optimizing priority assignments such as OPA (Audsley 1991 ) and RPA (Davis and Burns 2007 ). However, to our knowledge, none of the existing works offer ways to analyze trade-offs among equally viable priority assignments with respect to safety margins and the satisfaction of constraints. Nevertheless, we attempted to compare OPAM with an extension of an existing method, e.g., RPA (Davis and Burns 2007 ). To do so, we first applied an exhaustive schedulability analysis technique to the ESAIL subject – our motivating case study – in order to verify whether the ESAIL tasks are schedulable for a given priority assignment. Note that existing priority assignment techniques are built on such schedulability analysis methods, which are therefore a prerequisite. We chose UPPAAL (Behrmann et al. 2004 ), a model checker, for schedulability analysis as it has been used in real-time system studies (Mikučionis et al. 2010 ; Yu et al. 2010 ; Yalcinkaya et al. 2019 ). However, our experiment results using UPPAAL for ESAIL showed that it was not able to complete the analysis task, even after 5 days of execution, for a single priority assignment. We were therefore not able to perform experimental comparisons with existing priority assignment methods. Since this evaluation is not the main focus of this article, we point the reader to the UPPAAL specification of ESAIL available online (Lee et al. 2021 ).

Recall from Section  6.2 that OPAM assigns tasks’ WCETs to their execution times when it simulates the worst-case executions of tasks while varying task arrival times. In many real-time systems studies (Briand et al. 2005 ; Guan et al. 2009 ; Lin et al. 2009 ; Anssi et al. 2011 ; Zeng et al. 2014 ; Di Alesio et al. 2015 ; Du̇rr et al. 2019 ), static WCETs are often used instead of varying task execution times for the purpose of real-time analysis. For example, practitioners typically use WCETs to estimate the lowest bound of CPU utilization required to properly apply the rate monotonic scheduling policy (Fineberg and Serlin 1967 ) to their systems. Similarly, OPAM assumes that near-worst-case schedule scenarios can be simulated by assigning tasks’ WCETs to their execution times and varying tasks’ arrival times using search. A near-worst-case schedule scenario entails that the magnitude of deadline misses is maximized when tasks execute as per this scenario. Under this working assumption, we were able to empirically evaluate the sanity, coevolution, scalability, and usefulness aspects of OPAM (see Section  7 ). The results indicate that OPAM is a promising and useful tool. However, the formal proof of whether or not the WCET assumption holds in the system model described in Section  3 requires complex analysis, accounting for varying task arrival times, triggering relationships, resource dependencies, and multiple cores. When task execution times need to be varied during simulation, engineers can adapt OPAM by utilizing Monte-Carlo simulation (Kroese et al. 2014 ) to account for such variations.

The main threat to external validity is that our results may not generalize to other systems. We mitigate potential biases and errors in our experiments by drawing on real industrial subjects from different domains and several synthetic subjects. Specifically, we selected two subjects from the aerospace domain, two from the automotive domain, and two from the avionics domain. The positive feedback obtained from LuxSpace and the encouraging results from our industrial case studies indicate that OPAM is a scalable and practical solution. Furthermore, we believe OPAM introduces a promising avenue for addressing the problem of priority assignment by applying coevolutionary algorithms, even for systems that use other scheduling policies, e.g., priority inheritance. In order for OPAM to support different scheduling policies, the main requirement is to replace the existing simulator (described in Section  6 ) with a new simulator supporting the desired scheduling policy. In our approach, the coevolution part of OPAM is separated from the scheduling policy, which is contained in the simulator. Hence, we deem the expected changes for the coevolution part of OPAM to be minimal. Future studies are nevertheless necessary to investigate how OPAM can be adapted to find near-optimal priority assignments for other real-time systems in different contexts.

8 Conclusion

We developed OPAM, a priority assignment method for real-time systems, that aims to find equally viable priority assignments that maximize the magnitude of safety margins and the extent to which engineering constraints are satisfied. OPAM uses a novel approach, based on multi-objective, competitive coevolutionary search, that simultaneously evolves different species, i.e., populations of priority assignments and stress test scenarios, that compete with one another with opposite objectives, the former trying to minimize chances of deadline misses while the latter attempts to maximize them. We evaluated OPAM on a number of synthetic systems as well as six industrial systems from different domains. The results indicate that OPAM is able to find significantly better solutions than both those manually defined by engineers based on expert knowledge and those obtained by our baselines: random search and sequential search. Further, OPAM scales linearly with the number of tasks in a system and the time required to simulate task executions. Execution times on our industrial systems are practically acceptable.

In the future, we will continue to study the problem of optimal priority assignment by accounting for (1) priority assignments that change dynamically, (2) WCET value ranges that account for non-deterministic computation times, (3) interrupt handling routines that execute differently compared to real-time tasks, and (4) hybrid scheduling policies that combine multiple standard policies. We also plan to develop a real-time task modeling language to specify task characteristics such as resource dependencies, triggering relationships, engineering constraints, and behaviors of real-time tasks and to facilitate real-time system analysis, e.g., optimal priority assignment and schedulability analysis. In addition, we would like to incorporate additional analysis capabilities into OPAM in order to verify whether or not a system satisfies the required properties, e.g., schedulability of tasks and absence of deadlocks, for a given priority assignment. For example, statistical model checking (Legay et al. 2010 ) may allow us to verify whether tasks meet their deadlines for a given priority assignment with a probabilistic guarantee. In the long term, we plan to more conclusively validate the usefulness of OPAM by applying it to additional case studies in different application domains.

Bosch GmbH: https://www.bosch.com/

Continental AG: https://www.continental.com

ENSMA: https://www.ensma.fr/

IBM: https://www.ibm.com/

ESA: https://www.esa.int/

Abdessalem RB, Panichella A, Nejati S, Briand LC, Stifter T (2020) Automated repair of feature interaction failures in automated driving systems. In: Proceedings of the 29th ACM SIGSOFT international symposium on software testing and analysis (ISSTA’20), pp 88–100

Andrade SS, Macêdo RJdA (2013) A search-based approach for architectural design of feedback control concerns in self-adaptive systems. In: Proceedings of the 2013 IEEE 7th international conference on self-adaptive and self-organizing systems (SASO’13), pp 61–70

Anssi S, Tucci-Piergiovanni S, Kuntz S, Gérard S, Terrier F (2011) Enabling scheduling analysis for AUTOSAR systems. In: Proceedings of the 14th IEEE international symposium on Object/Component/Service-Oriented real-time distributed computing (ISORC’11), pp 152–159

Arcuri A, Briand LC (2014) A hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Software Testing, Verification and Reliability 24(3):219–250

Article   Google Scholar  

Arcuri A, Fraser G (2011) On parameter tuning in search based software engineering. In: Proceedings of the 3th international symposium on search based software engineering (SSBSE’11), pp 33–47

Arcuri A, Iqbal MZ, Briand LC (2010) Black-box system testing of real-time embedded systems using random and search-based testing. In: Proceedings of the IFIP international conference on testing software and systems (ICTSS’10), vol 6435, pp 95–110

Arpaci-Dusseau RH, Arpaci-Dusseau AC (2018) Operating Systems: Three Easy Pieces, 1st edn., Arpaci-Dusseau Books

Audsley NC (1991) Optimal priority assignment and feasibility of static priority tasks with arbitrary start times. Tech. rep. Dept. Computer Science, University of York

Audsley NC (2001) On priority assignment in fixed priority scheduling. Inform Process Lett 79(1):39–44

Article   MATH   Google Scholar  

Baruah SK, Burns A, Davis RI (2011) Response-time analysis for mixed criticality systems. In: Proceedings of the 2011 IEEE 32nd real-time systems symposium (RTSS’11), pp 34–43

Behrmann G, David A, Larsen KG (2004) A tutorial on UPPAAL. In: Formal methods for the design of real-time systems: international school on formal methods for the design of computer, communication, and software systems, pp 200–236

Bernat G, Burns A (2001) Weakly hard real-time systems. IEEE Trans Comput 50(4):308–321

Article   MathSciNet   MATH   Google Scholar  

Boussaa M, Kessentini W, Kessentini M, Bechikh S, Ben Chikha S (2013) Competitive coevolutionary code-smells detection. In: Proceedings of the 5th international symposium on search based software engineering (SSBSE’13), pp 50–65

Briand LC, Labiche Y, Shousha M (2005) Stress testing real-time systems with genetic algorithms. In: Proceedings of the 7th annual conference on genetic and evolutionary computation (GECCO’05), pp 1021–1028

Chen T, Li K, Bahsoon R, Yao X (2018) FEMOSAA: Feature-guided and knee-driven multi-objective optimization for self-adaptive software. ACM Transactions on Software Engineering and Methodology 27(2):1–50

Chen TY, Kuo FC, Merkel RG, Tse TH (2010) Adaptive Random Testing: The ART of test case diversity. Journal of Systems and Software 83(1):60–66

Chu Y, Burns A (2008) Flexible hard real-time scheduling for deliberative AI systems. Real-Time Systems 40(3):241–263

Davis RI, Bertogna M (2012) Optimal fixed priority scheduling with deferred pre-emption. In: Proceedings of the 2012 IEEE 33rd real-time systems symposium (RTSS’12), pp 39–50

Davis RI, Burns A (2007) Robust priority assignment for fixed priority real-time systems. In: Proceedings of the 28th IEEE international real-time systems symposium (RTSS’07), pp 3–14

Davis RI, Burns A (2009) Robust priority assignment for messages on Controller Area Network (CAN). Real-Time Systems 41(2):152–180

Davis RI, Burns A (2011) Improved priority assignment for global fixed priority pre-emptive scheduling in multiprocessor real-time systems. Real-Time Systems 47(1):1–40

Davis RI, Zabos A, Burns A (2008) Efficient exact schedulability tests for fixed priority real-time systems. IEEE Trans Comput 57(9):1261–1276

Davis RI, Cucu-Grosjean L, Bertogna M, Burns A (2016) A review of priority assignment in real-time systems. Journal of Systems Architecture 65:64–82

Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2):182–197

Di Alesio S, Gotlieb A, Nejati S, Briand LC (2012) Testing deadline misses for real-time systems using constraint optimization techniques. In: Proceedings of the 2012 IEEE 5th international conference on software testing, verification and validation (ICST’12), pp 764–769

Di Alesio S, Nejati S, Briand LC, Gotlieb A (2013) Stress testing of task deadlines: A constraint programming approach. In: Proceedings of the IEEE 24th international symposium on software reliability engineering (ISSRE’13), pp 158–167

Di Alesio S, Briand LC, Nejati S, Gotlieb A (2015) Combining genetic algorithms and constraint programming to support stress testing of task deadlines. ACM Transactions on Software Engineering and Methodology 25(1):1–37

Durillo JJ, Nebro AJ (2011) JMetal: A java framework for multi-objective optimization. Advances in Engineering Software 42(10):760–771

Du̇rr M, Bru̇ggen G V D, Chen KH, Chen JJ (2019) End-to-end timing analysis of sporadic cause-effect chains in distributed systems. ACM Transactions on Embedded Computing Systems 18(5s):1–24

Emberson P, Stafford R, Davis RI (2010) Techniques for the synthesis of multiprocessor tasksets. In: Proceedings of the 1st international workshop on analysis tools and methodologies for embedded and real-time systems (WATERS’10), pp 6–11

Ficici SG (2004) Solution concepts in coevolutionary algorithms. Ph.d. thesis, Brandeis University, Department of Computer Science, Waltham, MA

Fineberg MS, Serlin O (1967) Multiprogramming for hybrid computation. In: Proceedings of the AFIPS fall joint computing conference (AFIPS’67), pp 1–13

Gendreau M, Potvin JY (2010) Handbook of Metaheuristics. Springer

George L, Rivierre N, Spuri M (1996) Preemptive and non-preemptive real-time uniprocessor scheduling. Research Report RR-2966, INRIA, projet REFLECS

Goldberg DE, Lingle R (1985) Alleleslociand the traveling salesman problem. In: Proceedings of the 1st international conference on genetic algorithms, pp 154–159

Grass W, Nguyen THC (2018) Improved response-time bounds in fixed priority scheduling with arbitrary deadlines. Real-Time Systems 54(1):1–30

Guan N, Stigge M, Yi W, Yu G (2009) New response time bounds for fixed priority multiprocessor scheduling. In: Proceedings of the 2009 30th IEEE international real-time systems symposium (RTSS’09), pp 387–397

Hatvani L, Afshar S, Bril RJ (2018) Optimal priority and threshold assignment for fixed-priority preemption threshold scheduling. ACM SIGBED Review 15(1):43–49

Hemmati H, Arcuri A, Briand LC (2013) Achieving scalable model-based testing through test case diversity. ACM Transactions on Software Engineering and Methodology 22(1):1–42

Ishibuchi H, Masuda H, Tanigaki Y, Nojima Y (2015) Modified distance calculation in generational distance and inverted generational distance. In: Proceedings of the 8th international conference on evolutionary multi-criterion optimization (EMO’15), pp 110–125

Islam MM, Marchetto A, Susi A, Scanniello G (2012) A multi-objective technique to prioritize test cases based on latent semantic indexing. In: Proceedings of the 2012 16th European conference on software maintenance and reengineering (CSMR’12), pp 21–30

Knowles JD, Corne DW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation 8(2):149–172

Kroese DP, Brereton TJ, Taimre T, Botev ZI (2014) Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics 6:386–392

Kwiatkowska M, Norman G, Parker D (2011) PRISM 4.0: Verification of probabilistic real-time systems. In: Proceedings of the 23rd international conference on computer aided verification (CAV’11), pp 585–591

Langdon WB, Harman M, Jia Y (2010) Efficient multi-objective higher order mutation testing with genetic programming. Journal of Systems and Software 83(12):2416–2430

Lee H, Lee J, Yeom I, Woo H (2020a) Panda: Reinforcement learning-based priority assignment for multi-processor real-time scheduling. IEEE Access 8:185570–185583

Lee J, Shin SY, Nejati S, Briand LC, Parache YI (2020b) Schedulability analysis of real-time systems with uncertain worst-case execution times. arXiv: abs/2007.10490

Lee J, Shin SY, Nejati S, Briand LC (2021) [Evaluation package] Optimal priority assignment method for real-time systems. https://github.com/SNTSVV/OPAM

Legay A, Delahaye B, Bensalem S (2010) Statistical model checking: An overview. In: Proceedings of the international conference on runtime verification (RV’10), pp 122–135

Leung JYT, Whitehead J (1982) On the complexity of fixed-priority scheduling of periodic, real-time tasks. Performance Evaluation 2(4):237–250

Li M, Chen T, Yao X (2020) How to evaluate solutions in Pareto-based search-based software engineering? A critical review and methodological guidance. IEEE Transactions on Software Engineering

Lin M, Xu L, Yang LT, Qin X, Zheng N, Wu Z, Qiu M (2009) Static security optimization for real-time systems. IEEE Transactions on Industrial Informatics 5(1):22–37

Liu CL, Layland JW (1973) Scheduling algorithms for multiprogramming in a hard-real-time environment. Journal of the ACM 20(1):46–61

Liu JWS (2000) Real-Time Systems, 1st edn. Prentice Hall PTR

Locke CD, Vogel DR, Lucas L, Goodenough JB (1990) Generic avionics software specification. Tech. rep., DTIC Document

Luke S (2013) Essentials of Metaheuristics. 2nd edn, Lulu, available for free at http://cs.gmu.edu/~sean/book/metaheuristics/

LuxSpace (2021) ESAIL. https://luxspace.lu/triton-2/

Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics 18(1):50–60

Marchetto A, Islam MM, Asghar W, Susi A, Scanniello G (2016) A multi-objective technique to prioritize test cases. IEEE Transactions on Software Engineering 42(10):918–940

Meneghini IR, Guimarães F G, Gaspar-Cunha A (2016) Competitive coevolutionary algorithm for robust multi-objective optimization: The worst case minimization. In: Proceedings of the 2016 IEEE congress on evolutionary computation (CEC’16), pp 586–593

Mikučionis M, Larsen KG, Rasmussen JI, Nielsen B, Skou A, Palm SU, Pedersen JS, Hougaard P (2010) Schedulability analysis using UPPAAL: Herschel-Planck case study. In: Proceedings of the international symposium on leveraging applications of formal methods, verification and validation (ISoLA’10), pp 175–190

Nejati S, Briand LC (2014) Identifying optimal trade-offs between CPU time usage and temporal constraints using search. In: Proceedings of the 2014 international symposium on software testing and analysis (ISSTA’14), pp 351–361

Nejati S, Di Alesio S, Sabetzadeh M, Briand LC (2012) Modeling and analysis of CPU usage in safety-critical embedded systems to support stress testing. In: Proceedings of the 15th international conference of model driven engineering languages and systems (MODELS’12), vol 7590, pp 759–775

Nejati S, Adedjouma M, Briand LC, Hellebaut J, Begey J, Clement Y (2013) Minimizing CPU time shortage risks in integrated embedded software. In: Proceedings of the 28th IEEE/ACM international conference on automated software engineering (ASE’13), pp 529–539

Peng D, Shin KG, Abdelzaher TF (1997) Assignment and scheduling communicating periodic tasks in distributed real-time systems. IEEE Transactions on Software Engineering 23(12):745–758

Peraldi-Frati MA, Sorel Y (2008) From high-level modelling of time in MARTE to real-time scheduling analysis. In: Proceedings of the MODELS’08 workshop on model based architecting and construction of embedded systems (ACES-MB), vol 503, pp 129–144

Popovici E, Bucci A, Wiegand RP, De Jong ED (2012) Coevolutionary principles. In: Handbook of natural computing. Springer, pp 987–1033

Ralph P, bin Ali N, Baltes S, Bianculli D, Diaz J, Dittrich Y, Ernst N, Felderer M, Feldt R, Filieri A, de França BBN, Furia CA, Gay G, Gold N, Graziotin D, He P, Hoda R, Juristo N, Kitchenham B, Lenarduzzi V, Martínez J, Melegati J, Mendez D, Menzies T, Molleri J, Pfahl D, Robbes R, Russo D, Saarimäki N, Sarro F, Taibi D, Siegmund J, Spinellis D, Staron M, Stol K, Storey MA, Taibi D, Tamburri D, Torchiano M, Treude C, Turhan B, Wang X, Vegas S (2020) Empirical standards for software engineering research. 2010.03525

Sayyad AS, Goseva-Popstojanova K, Menzies T, Ammar H (2013) On parameter tuning in search based software engineering: A replicated empirical study. In: Proceedings of the 2013 3rd international workshop on replication in empirical software engineering research (RESER’13), pp 84–90

Shin SY, Nejati S, Sabetzadeh M, Briand LC, Zimmer F (2018) Test case prioritization for acceptance testing of cyber physical systems: A multi-objective search-based approach. In: Proceedings of the 27th ACM SIGSOFT international symposium on software testing and analysis (ISSTA’18), pp 49–60

Shin SY, Nejati S, Sabetzadeh M, Briand LC, Arora C, Zimmer F (2020) Dynamic adaptation of software-defined networks for IoT systems: A search-based approach. In: Proceedings of the IEEE/ACM 15th international symposium on software engineering for adaptive and self-managing systems (SEAMS’20), pp 137–148

Sivanandam SN, Deepa SN (2008) Introduction to Genetic Algorithms, 1st edn. Springer, Berlin

MATH   Google Scholar  

Tan SH, Yoshida H, Prasad MR, Roychoudhury A (2016) Anti-patterns in search-based program repair. In: Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering (FSE’16), pp 727–738

Tindell KW, Burns RA, Wellings A (1994) An extendible approach for analyzing fixed priority hard real-time tasks. Real-Time Systems 6(2):133–151

Traore K, Grolleau E, Cottet F (2006) Simpler analysis of serial transactions using reverse transactions. In: Proceedings of the international conference on autonomic and autonomous systems (ICAS’06), p 11

Vargha A, Delaney H (2000) A critique and improvement of the “CL” common language effect size statistics of McGraw and Wong. Journal of Educational and Behavioral Statistics 25(2):101–132

Google Scholar  

Varrette S, Bouvry P, Cartiaux H, Georgatos F (2014) Management of an academic HPC cluster: The UL experience. In: Proceedings of the 2014 international conference on high performance computing & simulation (HPCS’14), pp 959–967

Veldhuizen DAV, Lamont GB (1998) Multiobjective evolutionary algorithm research : A history and analysis. Tech. RepTR–98–03, Air Force Institute of Technology, Wright-Patterson AFB

Wang P, Huang J, Cui Z, Xie L, Chen J (2020) A gaussian error correction multi-objective positioning model with NSGA–II. Concurrency and Computation: Practice and Experience 32(5):1–16

Wegener J, Grochtmann M (1998) Verifying timing constraints of real-time systems by means of evolutionary testing. Real-Time Systems 15(3):275–298

Wegener J, Sthamer H, Jones BF, Eyres DE (1997) Testing real-time systems using genetic algorithms. Software Quality Journal 6(2):127–135

Weimer W, Nguyen T, Le Goues C, Forrest S (2009) Automatically finding patches using genetic programming. In: Proceedings of the 31st international conference on software engineering (ICSE’09), pp 364–374

Whitley D, Kauth J (1988) GENITOR: A different genetic algorithm. In: Proceedings of the 1988 rocky mountain conference on artificial intelligence, pp 118–130

Wilkerson JL, Tauritz D (2010) Coevolutionary automated software correction. In: Proceedings of the 12th annual conference on genetic and evolutionary computation (GECCO’10), pp 1391–1392

Wilkerson JL, Tauritz DR, Bridges JM (2012) Multi-objective coevolutionary automated software correction. In: Proceedings of the 14th annual conference on genetic and evolutionary computation (GECCO’12), pp 1229–1236

Yalcinkaya B, Nasri M, Brandenburg BB (2019) An exact schedulability test for non-preemptive self-suspending real-time tasks. In: Proceedings of the 2019 design, automation & test in europe conference & exhibition (DATE’19), pp 1228–1233

Yu F, Li G, Xiong N (2010) Schedulability analysis of multi-processor real-time systems using UPPAAL. In: Proceedings of the 2nd international conference on information science and engineering (ICISE’10), pp 1–6

Zeng H, Natale MD, Zhu Q (2014) Minimizing stack and communication memory usage in real-time embedded applications. ACM Transactions on Embedded Computing Systems 13(5s):1–25

Zhang F, Burns A (2009) Schedulability analysis for real-time systems with EDF scheduling. IEEE Trans Comput 58(9):1250–1258

Zhao Y, Zeng H (2017) The virtual deadline based optimization algorithm for priority assignment in fixed-priority scheduling. In: Proceedings of the 2017 IEEE real-time systems symposium (RTSS’17), pp 116–127

Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3(4):257–271

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Acknowledgements

We thank Yago Isasi Parache, LuxSpace, for his support in conducting our industrial case study. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 694277), and NSERC of Canada under the Discovery and CRC programs. The experiments presented in this paper were carried out using the HPC facilities of the University of Luxembourg (Varrette et al. 2014 ) – see http://hpc.uni.lu .

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Jaekwon Lee, Seung Yeob Shin, Shiva Nejati & Lionel C. Briand

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Communicated by: Aldeida Aleti, Annibale Panichella and Shin Yoo

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Lee, J., Shin, S.Y., Nejati, S. et al. Optimal priority assignment for real-time systems: a coevolution-based approach. Empir Software Eng 27 , 142 (2022). https://doi.org/10.1007/s10664-022-10170-1

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DOI : https://doi.org/10.1007/s10664-022-10170-1

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Priority Assignment to Tasks in Operating System

The practice of giving each task or process in an operating system a priority level is known as a priority assignment. A priority level is a number that represents the relative urgency or significance of an activity or process in comparison to other tasks.

When there are numerous tasks that are ready to run, the operating system utilizes the priority level to decide which job should be done next. Priority-level tasks are carried out before those with lower priority levels. This guarantees that the system runs effectively and that the most important activities are finished first.

The assignment of priorities may be static or dynamic. Static priority assignment assigns a priority level to each task at the moment of creation, and this priority level stays constant for the duration of the job. The priority level of a job can be changed dynamically at runtime depending on the task's relative relevance, execution duration, and resource utilization.

In order to guarantee that the operating system responds fast to significant events and that resources are allocated effectively, priority assignment is a crucial component of operating system architecture. Priority inversion, where a lower-priority task prevents the execution of a higher-priority task, and priority starvation, where a task with a lower priority level is never executed because higher-priority tasks are constantly running, are examples of problems that can arise from improper priority assignment.

Types of Priorities

A process or task's importance in the system is determined by its priority. A procedure with a higher priority will be given more resources and will be carried out before those with a lower priority. Ordinarily, priorities are ranked from 0 to 127, with 127 being the highest priority and 0 being the lowest.

In operating systems, many forms of priority are utilized. As follows −

Static Priorities

In this style of assigning priorities, a process's or a task's priority is set and does not alter as it is being executed. Typically, the system administrator or the developer determines the priority when a process is created. Static priorities are simple to design, but if they are not allocated properly, they may result in ineffective resource use.

Dynamic Priorities

With this sort of priority assignment, a process's or task's priority can change while it runs, depending on things like how the process behaves, how much CPU it is using, and how much system load there is. Dynamic priorities make it possible to employ resources more effectively and guarantee that the most crucial activities are completed first.

Priority Inversion

When a lower priority job prevents or delays a higher priority task, it is known as priority inversion. This may occur when a low-priority process is using a resource that a high-priority task is awaiting. In this case, a low-priority task may hog a resource for a considerable amount of time, delaying the execution of a high-priority task.

Priority inversion can be avoided using a number of tactics, including priority inheritance, priority ceilings, and priority inheritance combined with ceilings. Priority inheritance momentarily increases the low-priority activity's priority to that of the high-priority task when it has a resource that the high-priority job requires. The procedure of assigning a shared resource a priority ceiling higher than the priority of any activity that may use the resource is referred to as setting a priority ceiling. Priority inheritance with ceiling, which combines the two approaches, ensures that priority inheritance only takes place when the low-priority task is in possession of a shared resource that is subject to a priority ceiling.

Priority Scheduling Algorithms

Priority scheduling algorithms are used to determine which tasks or activities should be completed first and in what order. Several priority scheduling strategies are used by various operating systems, including −

First-Come-First-Serve (FCFS)

With this algorithm, tasks are completed in the order they are received. A job's priority is not taken into account, therefore if there are a lot of low-priority tasks waiting in line, the task with the highest priority may have to wait a very long time.

Shortest-Job-First (SJF)

The job that can be completed in the smallest amount of time comes first in this method. A task's priority is not taken into consideration, therefore the job with the greatest priority could have to wait a long time if its execution takes longer than a task with a lower priority.

Priority Scheduling

This algorithm prioritizes activities, with higher priority tasks being carried out before lower priority duties. The same-priority tasks are completed in FCFS sequence.

Round Robin Scheduling

Using this approach, jobs are carried out in a cycle where each job has a set window of time (or quantum) to run. If a job does not complete within its time slice, it is preempted and put at the end of the line. In this method, priority is considered, and tasks with higher priorities are given preference over those with lower priorities.

Multilevel Feedback Queue Scheduling

According to this method, tasks are separated into several queues according to their importance. Tasks are transferred to lower priority queues if they do not finish within their time slice, and the highest priority queue is completed first. Tasks that consume a lot of CPU time are transferred to lower priority queues so that other jobs can have a chance to execute, and the priority of a task is dynamically altered based on its behavior.

Real-Time Operating Systems

Real-Time Operating Systems (RTOS) are types of operating systems created to manage operations with exact timing constraints, such as those present in industrial control systems, medical equipment, and automotive systems. Priority assignment in RTOS is essential to ensuring that the most important activities are completed on schedule. The typical scheduling mechanism used by RTOS assigns jobs static priorities at the time of creation.

Moreover, RTOS employ a number of strategies to guarantee that higher priority operations do not obstruct or delay vital jobs. These methods include proactive scheduling, deadline-based scheduling, and priority inversion prevention.

Applications

Various scheduling algorithms and priority assignment methods are used by various operating systems. These are a few instances −

The scheduling algorithm used by Windows allocates priorities depending on the priority levels of the threads. From 0 to 31, there are several degrees of thread priority; higher levels have greater priorities. Moreover, Windows has a preemptive scheduling system that makes sure higher priority threads take precedence over lower priority threads.

A dynamic priority scheduling technique employed by Linux determines a task's priority depending on how much CPU time it has consumed. Moreover, Linux makes use of the Fully Fair Scheduler (CFS), which distributes CPU time across jobs according to priority and CPU consumption.

The scheduling algorithm used by Android allocates priorities depending on the levels of each process's priority. Between -20 and 19, there are several levels of process priority; lower numbers indicate higher priorities. The O(1) scheduler, which is also used by Android, makes sure that higher priority jobs take precedence over lower priority ones.

Each operating system must include priority assignment since it guarantees effective resource usage and enhances system performance. Depending on their needs, several operating systems employ various priority assignment strategies and scheduling algorithms. When a lower priority job prevents or delays a higher priority task, it is known as priority inversion. Priority inversion can be prevented using a number of strategies, including priority inheritance, priority ceilings, and priority inheritance with ceiling. Fixed-priority scheduling algorithms and other methods are used by real-time operating systems to make sure that crucial operations are completed on schedule. Generally, the effective and dependable operation of contemporary computer systems depends on priority assignment.

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