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Reliability and high availability in cloud computing environments: a reference roadmap

• Mohammad Reza Mesbahi 1 ,
• Amir Masoud Rahmani   ORCID: orcid.org/0000-0001-8641-6119 1 , 2 &
• Mehdi Hosseinzadeh 3  

Human-centric Computing and Information Sciences volume  8 , Article number:  20 ( 2018 ) Cite this article

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Reliability and high availability have always been a major concern in distributed systems. Providing highly available and reliable services in cloud computing is essential for maintaining customer confidence and satisfaction and preventing revenue losses. Although various solutions have been proposed for cloud availability and reliability, but there are no comprehensive studies that completely cover all different aspects in the problem. This paper presented a ‘Reference Roadmap’ of reliability and high availability in cloud computing environments. A big picture was proposed which was divided into four steps specifying through four pivotal questions starting with ‘Where?’, ‘Which?’, ‘When?’ and ‘How?’ keywords. The desirable result of having a highly available and reliable cloud system could be gained by answering these questions. Each step of this reference roadmap proposed a specific concern of a special portion of the issue. Two main research gaps were proposed by this reference roadmap.

Introduction

It was not so long ago that applications were entirely developed by organizations for their own use, possibly exploiting components/platforms developed by third parties. However, with service-oriented architecture (SOA), we moved into a new world which applications could delegate some of their functionalities to already existing services developed by third parties [ 1 ].

For meeting ever-changing business requirements, organizations have to invest more in time and budget for scaling up IT infrastructures. However, achieving this aim by own premises and investments not only is not cost-effective but also organizations will not be able to have an optimal resource utilization [ 2 ]. Therefore, these challenges have forced companies to seek some new alternative technology solutions. One of these modern technologies is cloud computing, which focuses on increasing computing power to execute millions of instructions per seconds.

Nowadays, cloud computing and its services are at the top of the list of buzzwords in the IT world. It is a recent trend in IT that can be considered as a paradigm shift for providing IT & computing resources through the network. One of the best and most popular definitions of cloud computing is the NIST definition proposed in 2009 and updated in 2011. According to this definition, “Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [ 3 ].

Recent advances in cloud computing are pushing virtualization more than ever. In other words, cloud computing services can be considered as a significant step towards realizing the utility computing concept [ 4 ]. In such a computing model, services can be accessed by users regardless of where they are hosted or how they are delivered.

Over the years, computing trends such as cluster computing, grid computing, service-oriented computing and virtualization have gained maturity but cloud computing is still in infancy, and experiences lack of complete standards and solutions [ 5 ]. Therefore, the following critical issues are introduced by cloud business models and technologies including load balancing [ 6 , 7 ], security [ 8 , 9 ], energy-efficiency [ 10 , 11 , 12 ], workflow scheduling [ 13 , 14 , 15 , 16 , 17 ], data/service availability, license management, data lock-in and API design [ 1 , 2 , 5 , 18 , 19 , 20 ].

High Availability (HA) and reliability in cloud computing services are some of the hot challenges. The probability that a system is operational in a time interval without any failures is represented as the system reliability, whereas the availability of a system at time ‘t’ is referred to as the probability that the system is up and functional correctly at that instance in time [ 21 , 22 ]. HA for cloud services is essential for maintaining customer’s confidence and preventing revenue losses due to service level agreement (SLA) violation penalties [ 23 , 24 ]. In recent years, cloud computing environments have received significant attention from global business and government agencies for supporting critical mission systems [ 25 ]. However, the lack of reliability and high availability of cloud services is quickly becoming a major issue [ 25 , 26 ]. Research reports express that about $285 million have been lost yearly due to cloud service failures and offering availability of about 99.91% [ 26 ].

Cloud computing service outage can seriously impact workloads of enterprise systems and consumer data and applications. Amazon’s EC2 outage on April, 2011 is an example of one of the largest cloud disasters. Several days of Amazon cloud services unavailability resulted in data loss of several high profile sites and serious business issues for hundreds of IT managers [ 27 ]. Furthermore, according to the CRN reports [ 28 ], the 10 biggest cloud service failure of 2017, including IBM’s cloud infrastructure failure on January 26, GitLab’s popular online code repository service outage on January 31, Facebook on February 24, Amazon Web Services on February 28, Microsoft Azure on March 16, Microsoft Office 365 on March 21 and etc., caused production data loss, and prevented customers from accessing their accounts, services, projects and critical data for very long and painful hours. In addition, credibility of cloud providers took a hit in these service failures and unavailability.

Although many research papers and studies have been conducted in recent years such as studies in [ 26 , 29 , 30 , 31 , 32 , 33 , 34 ], but each of these previous works focused on a special aspect of HA in cloud computing and there are no comprehensive studies, which cover all aspects of HA problem in cloud computing environment based on all cloud actors’ requirements. In this paper, a comprehensive study results about cloud computing reliability and HA problems as a ‘Reference Roadmap’ for future researchers will be proposed.

This research aims to offer a comprehensive strategy for providing high availability and reliability while guaranteeing less performance degradation in cloud computing datacenters. Therefore, by satisfying the users’ requirements and providing services according to the SLA and preventing SLA violation penalties, providers can make a huge profit [ 35 ]. To achieve this goal, all possible aspects to this issue will be studied by proposing a big picture of the problem (for clarifying the possible future approaches). In this research, the proposed research gaps will be puzzled out by completing the pieces of our big picture through answering four questions starting with ‘Where?’, ‘Which?’, ‘When?’ and ‘How?’ question words. In the rest of this paper, a proposed reference roadmap will be introduced and its different aspects will be described. Then the possible primary solutions for these proposed questions will be introduced. The main contributions of this study can be considered as follows:

A reference road map for cloud high availability and reliability is proposed.

A big picture is proposed through dividing the problem space into four major parts.

A comprehensive cloud task taxonomy and failure classification are presented.

Research gaps which were neglected in the literature review are identified.

The rest of this paper is as follows. The research background and literature review is presented in “ Research background ”. The proposed reference roadmap in terms of research “Big Picture” is presented in “ Proposed reference roadmap ”. Discussion and open issues section is presented in “ Discussion and open issues ”. The paper is concluded in “ Conclusion ”.

Research background

Cloud computing provides the context of offering virtualized computing resources and services in a shared and scalable environment through the network on a pay-as-you-go model. By rapid adoption of cloud computing, a large proportion of worldwide IT companies and government organizations have adopted cloud services for various purposes including hosting the mission-critical applications and thus critical data [ 34 ]. In order to support these mission-critical applications and data, there is need to provide dependable cloud computing environments.

In order to study dependability of cloud computing, the major cloud computing system (CCS) dependability attributes should be identified which can quantify the dependability of cloud in different aspects. Some important attributes for dependable cloud environments have been mentioned in [ 36 ] and include availability, reliability, performability, security, and recoverability.

Five major actors and related roles in cloud environments are described in the NIST Cloud Computing Standards Roadmap document [ 37 ]. These five participating actors are cloud provider, cloud consumer, cloud broker, cloud carrier and cloud auditor [ 37 ]. Table  1 presents the definitions of these actors. Consumers and Providers are two main roles among these actors which are significantly considerable in the most cloud computing scenarios. Therefore, this research paper focuses on these two actors.

Pan and Hu [ 36 ] proposed a summary of the relative strengths of dependency on different dependability attributes for each class of actors shown in Table  1 . This empirical analysis shows that there are three ‘Availability’, ‘Reliability’, and ‘Performability’ critical requirements for cloud consumers and providers. Thus, on one hand, from the consumers’ viewpoint, there is a great requirement for highly available and reliable cloud services, but on the other hand, while cloud providers understand the necessity for providing highly available and reliable services for meeting quality of services (QoS) due to the SLA, they also prefer to have highly utilized systems to achieve more profits [ 35 ]. Under these considerations, providing dependable cloud environments which can meet the desires of both cloud consumers and providers is a new challenge.

There are many different design principles such as ‘Eliminating Single Point of Failure’ [ 27 , 38 ], ‘Disaster Recovery’ [ 39 , 40 ] and ‘Real-Time and Fast Failure Detection’ [ 41 , 42 , 43 ] that can help achieve high availability and reliability in cloud computing environments. The single point of failure (SPOF) in cloud computing datacenters can occur in both software and hardware level. Single point of failure can be interpreted as a probable risk which can cause the entire system failure. Applying redundancy is an important factor for avoiding SPOFs [ 27 ]. In simple words, every vital component should exist in more than one instance. At the occurrence of a disaster at a main component in a cloud datacenter, system operations can be switched to backup services and efficient techniques are required for data backup and recovery [ 40 ]. The time taken to detect a failure is one of the key factors in the cloud computing environments. So, fast and real-time failure detection to identify or predict a failure in the early stages is one of the most important principles to achieving high availability and reliability in cloud systems [ 42 , 43 ]. Moreover, there are some new trends in cloud computing such as SDN-based technology like Espresso that makes cloud infrastructures more reliable and available in the network level [ 44 ].

A systematic review of high availability in cloud computing was undertaken by Endo et al. [ 45 ]. This study aimed to discuss high availability mechanisms and important related research questions in cloud computing systems. From the results, the three most useful HA solutions are ‘Failure Detection’, ‘Replication’, and ‘Monitoring’. In addition, the review results show that ‘Experiment’ approach is used more than other approaches for evaluating the HA solutions. However, the paper proposed some research questions and tried to answer these questions, but the study results are more similar to the study mapping review. Furthermore, it did not consider the different cloud actors’ requirements in terms of high availability.

Liu et al. [ 46 ] applied an analytical modeling and sensitivity analysis for investigating the effective factors on cloud infrastructure availability such as repair policy and system parameters. In this study, the replication method was used to provide physical machine availability. Two different repair policies were considered in this study, and a Stochastic Reward Nets availability model was developed for each policy. The numerical results of this study showed that both policies provide the same level of availability but with the different cost level. The system availability was assessed through modeling without considering the failure types in this paper. In addition, the limited number of hot pares and repair policies cannot be sufficient to evaluate the large-scale cloud environments.

The availability and performance of storage services in private cloud environments were evaluated in [ 47 ]. In this study, a hierarchical model was applied for evaluation which consists of Markov chain, stochastic Petri Nets and reliability block diagrams. The result of this study showed that the adoption of redundancy could reduce the probability that timeouts occurred and users were attended to due to failures.

An et al. [ 32 ] presented a system architecture and framework of fault tolerance middleware to provide high availability in cloud computing infrastructures. This middleware uses the virtual machine replicas according to a user defined algorithm for replication. Proposing an optimal replica placement in cloud infrastructure is an important issue in this study. So, authors developed an algorithm for online VM replica placement.

Snyder et al. [ 48 ] presented an algorithm for evaluating the reliability and performance of cloud computing datacenters. In this study, a non-sequential Monte Carlo simulation was used to analyze the system reliability. This paper demonstrated that using this approach can be more efficient and flexible for cloud reliability evaluation when there is a set of discrete resources.

A cloud scoring system was developed in [ 49 ] that can integrate with a Stochastic Petri Net model. In this study, while an analytical model was used for evaluating the application deployment availability, the scoring system can suggest the optimal HA-aware option according to the energy efficiency and operational expenditure. The proposed model in this study can consider different types of failures, repair policies, redundancy, and interdependencies of application’s components. One of the main contributions of this study is proposing an extensible integrated scoring system for offering the suitable deployment based on the users’ requirements.

A comparative evaluation of two redundancy and proactive fault tolerance techniques was proposed in [ 50 ], based on the cloud providers’ and consumers’ requirements. This evaluation was proposed in terms of cloud environment’s availability based on the consumers’ viewpoint and cost of energy from the providers’ viewpoint. The result of this study showed that the proactive fault tolerance methods can be better than traditional redundancy technique in terms of cloud consumers’ costs and execution success rate.

A framework for amending GreenCloud simulator was proposed in [ 51 ] to support the high availability features in simulation processes. In this study, the necessity of a simulator that can provide the HA features was addressed. Then, the phased communication application (PCA) scenario was implemented to evaluate the HA features, workload modeling and scheduling.

Table  2 presents a comparative analysis of some availability and reliability solutions. The comparative factors in this table are main idea, advantages, challenges and evaluation metrics.

The lack of evaluating solutions to quantitatively assess the availability of provided cloud services is one of the main issues and gaps. In addition, according to the literature review, it seems that VM performance overhead [ 52 ] can affect the system availability. Furthermore, the high availability common techniques such as VM migration can also affect the VM performance overhead. So, considering the mutual impact of VM performance overhead and high availability solutions in cloud environments is another important research gap in the current CCS study area.

Proposed reference roadmap

This section presents the proposed reference roadmap in terms of the big picture shown in Fig.  1 . The goal of this section is to cover all different aspects of the reliability and availability issues in cloud computing research area. So, the area in the big picture is divided into four major steps. Figure  1 illustrates the general scheme of the proposed big picture of our reference roadmap. This big picture represents all aspects and factors of high availability and reliability issues in cloud computing environments. In this big picture, a eucalyptus-based architecture illustrates the major and key components of a cloud computing environment. By determining cloud key components in this architecture we will be able to identify the most important factors that can affect system high availability and reliability. The main components include VM, node controller, cluster controller, storage controller, cloud controller and Walrus [ 23 ]. Four major steps to achieve a high available and reliable cloud system are posed in terms of four pivotal questions. In addition, other related issues such as cloud computing nodes’ performance have been considered in this big picture.

Proposed big picture of high availability and reliability issue in cloud computing environments

By proposing this big picture we aim to have a comprehensive look at the problem area and have a reference roadmap for taking each step to solve the problem in cloud computing environments. It is believed that by taking these four steps in terms of answering the four questions, a high available and reliable cloud computing system will be obtained and while satisfying the cloud consumers’ requirements, the providers’ concerns could also be considered.

In the rest of this section, the different parts of our proposed big picture will be explained in details. Each question will be discussed and current available approaches and solutions for each part will be proposed. In future works, we aim to study each part in more details as another independent research and use this big picture as a reference roadmap as mentioned earlier.

“ Where are the vital parts for providing HA in the body of cloud computing datacenters? ”

There are many different geographically distributed cloud clusters in a cloud environment. Cloud computing datacenters comprise of a different stack of components such as physical servers with heterogeneous hardware characteristics like different processor speed, disk and memory size [ 53 ]. In addition, based on the property of workload heterogeneity and dynamicity, datacenters run a vast number of applications with diverse characteristics. Particularly, an application can be divided into one or more processes running on dedicated virtual machines (VM) and the resource requirement differs from VM to VM [ 53 ]. Therefore, by posing the where question at the first step, we are trying to find out where the vital parts are with high priority and basic requirement for high availability. Figure  2 illustrates the proposed roadmap of the “Where” step. The following possible approaches are available for offering primary solutions for the where question.

Proposed roadmap of the “Where” step

• SLA-based approach

A service level agreement is simply defined as a part of a standardized service contract where a service is formally defined. It is an agreement about the quality of a provided service. The agreement describes terms regarding service usage rate and delivery which are agreed between the service providers and the consumers. SLAs contain the following parts [ 54 ]:

The agreement context. Signatory parties, generally the consumer and the provider, and possibly third parties entrusted to enforce the agreement, an expiration date, and any other relevant information.

A description of the offered services including both functional and non-functional aspects such as QoS.

Obligations agreement of each party, which is mainly domain-specific.

Policies: penalties incurred if a SLA term is not respected and SLA violation occurs.

Service level agreements can also be discussed at these three different levels:

Customer - based SLA It is a type of agreement with a single customer that covers all the necessary services. This is similar to the SLA between an IT service provider and the IT department of an organization for all required IT services.

Service - based SLA It is defined as a general agreement for all customers who are using the delivered services by the service provider.

Multi - level SLA This kind of agreement can be split into different levels, with each level addressing a different set of customers for the same services.

By using this approach, we can focus on the SLA in the ‘Multi-level SLA’ to find the clusters which are offering services to the users whose basic requirement and first priority to run their tasks are highly available in the entire cloud system. Then by determining these clusters it can be said that the vital parts of the system are known and if high availability can be provided for these clusters, it can be said that while providing a high available system, more benefits are gained by preventing SLA violation penalties.

• Using 80/20 Rule

Pareto principle or 80/20 rule is a useful rule in computer science world [ 55 , 56 , 57 ]. In simple words, it says that in anything, a few (about 20%) are vital and many (about 80%) are trivial. In the subject of cost in cloud computing environments, 80/20 rule can be used which says that 80% of the outcomes will come from 20% of your effort [ 58 ]. In addition, the 80/20 rule is leveraged in our previous study to provide a highly reliable architecture in cloud environments [ 57 ]. According to our research results, there are a reliable sub-cluster and a highly reliable zone in cloud computing datacenters which can be used to serve the most profitable request. This rule can be applied from two different perspectives:

80% of cloud service providers’ profits may come from 20% of customers.

80% of requested services consist of just 20% of the entire cloud providers’ services.

So in this step, we can limit the domain of providing high availability in cloud computing clusters in our study based on this assumption that the majority of cloud providers’ profits will be earned by offering 20% of the entire services to 20% of whole customers. Therefore, if the high availability in the clusters which belong to that 20% of customers can be guaranteed, then it can be claimed that cloud providers will make the maximum profits while offering a system with high availability.

• Task-based approach

Providing high availability for different requests and incoming workloads according to the requested task classification and through various suitable mechanisms for each task’s class is another approach for this step. So, if the cloud computing tasks can be classified according to their resource requirements (CPU, Memory, etc.), then cloud services and tasks high availability can be provided by hosting tasks in the suitable clusters to avoid task failure due to the resource limitation. For instance, a memory-intensive task will be forwarded into a cluster with sufficient memory resources. Therefore, tasks can be completed without any execution interruption related to the lack of memory errors.

There are many different types of application software and tasks that can be executed in a distributed environment such as high-performance computing (HPC) and high-throughput computing (HTC) applications [ 59 ]. It is required to provide a massively parallel infrastructure for running High-performance computing applications using multi-threaded and multi-process program models. Tightly coupled parallel jobs within a single machine can be executed efficiently using HPC applications. This kind of applications commonly uses message passing interface (MPI) to achieve the needed inter-process communication. On the other hand, distributed computing environments have had awesome achievements to execute loosely coupled applications using workflow systems. The loosely coupled applications may involve numerous tasks that can be separately scheduled on different heterogeneous resources to achieve the goals of the main application. Tasks could be large or small, compute-intensive or data-intensive and may be uniprocessor or multiprocessor. Moreover, these tasks may be loosely-coupled or tightly-coupled, heterogeneous or homogeneous and can have a static or dynamic nature. The aggregate number of tasks, the quantity of required computing resources and volumes of required data for processing could be small but also extremely large [ 59 ].

As mentioned earlier, the appearance of cloud computing guarantees providing highly available and efficient services to run applications like web applications, social networks, messaging apps etc. Providing this guarantee needs a scalable infrastructure including many computing clusters which are shared by various tasks with different requirements and quality of service in terms of availability, reliability, latency and throughput [ 60 ]. Therefore, to provide required service level (e.g. highly available services), a good understanding of task resource consumption (e.g. memory usage, CPU cycles, and storages) is essential.

As cloud computing has been in its infancy during the last years, the applications that will run on clouds are not well defined [ 59 ]. The Li and Qiu [ 61 ], expressed that the required amount of cloud infrastructure resources for current and future tasks can be predicted according to the major trends of the last decade of the large-scale and grid computing environments. First, singular jobs are mainly split into two data-intensive and compute-intensive tasks categories. It can be said that there are no tightly coupled parallel jobs. Second, the duration of individual tasks is dimensioning with every year; few tasks are still running for longer than 1 h and majority require only a few minute to complete. Third, compute-intensive jobs will be divided into Dag-based workflow and bags-of-tasks (BoTs). But data-intensive jobs may utilize several and different programming models.

A task classification has been done in [ 60 ] based on the tasks’ resource consumption running in the Google Cloud Backend. The results of this study are presented in Table  3 . Cloud computing tasks are classified based on their execution duration, number of required CPU cores and amount of required memory in this study. The essential amount of resources for each class of cloud tasks is represented with three major ‘Small’, ‘Med (Medium)’, and ‘Large’ factors, which are abbreviated as ‘s’, ‘m’ and ‘l’, respectively in the ‘Final Class’ column of Table  3 . Three words in this column represent the duration, cores and memory, correspondingly. In addition, ‘*’ means that all three factors are possible in this type of class. For instance, the Final Class ‘sm*’ refers to a class of tasks having an execution duration as ‘small’ (short-running tasks), the number of required CPU cores are ‘Med’ and they would consume ‘small’, ‘Med’ or ‘large’ amount of memory.

In addition, Foster et al. [ 59 ] characterize the clouds’ application to be loosely coupled, transaction oriented (small tasks in the order of milliseconds to seconds) and likely to be interactive (as opposed to batch-scheduled).

The proposed taxonomy of cloud computing tasks is presented in Fig.  3 . According to our studies, cloud tasks can be classified into two long-running and short-running tasks in terms of tasks’ duration. Therefore based on Table  3 , it can be said that task durations are bimodal, either somewhat less than 30 min or larger than 18 h [ 60 ]. Such behavior results from the characteristics and types of application tasks running on cloud infrastructures. There are two types of long-running tasks. The first are interactive or user-facing tasks which run continuously so as to respond quickly to a user request. The second type of long-running tasks is compute-intensive, such as processing web logs [ 60 ]. Interactive tasks consume a large amount of CPU and memory during periods of a high user request rate. It means that they are CPU-intensive and memory-intensive. In addition since they handle end-user interactions, they are likely latency-sensitive. There are several types of short-running tasks. These tasks dominate the task population. Some short duration tasks are highly parallel operations such as index lookups and searches [ 60 ]. We can also mention HPC tasks. Some tasks can be considered as short memory-intensive tasks which include memory intensive operations like map reduce workers that compute an inverted index. These types of tasks are specified as class 2 in Table  3 . Other tasks which include CPU-intensive operations are considered as short CPU-intensive tasks like map reduce workers which compute aggregation of a log. Finally, it can be said that typical data-intensive workloads consist of short-running, data-parallel tasks. For data-intensive applications, data should be moved across the network, which represents a potential bottleneck [ 62 ].

Cloud tasks taxonomy

According to the proposed cloud tasks taxonomy and previous discussion we can have more understanding of cloud tasks’ requirements and suggest the best approach for providing HA based on the tasks’ types. As the conclusion of this section, it can be said that interactive tasks require more HA from the customer perspectives. Because they are interacting with end users and consuming a large amount of CPU and memory during periods of high user request rate, then by detecting and providing more CPU and Memory resources for these group of tasks, their availability can be improved. In addition, as they are latency sensitive, then providing these resources should be done during a specific threshold. Likewise, for other types of tasks, related requirement and resources for being highly available can be provided.

“Which components play key roles to affect cloud computing HA and reliability?”

As mentioned earlier we identified main constituent components of a cloud computing architecture which are inspired by the Eucalyptus architecture. By answering to “Which” question, we are trying to know the system’s weak points of HA and reliability in this step as illustrated in Fig.  4 . After knowing these weak points, we will be able to think about how we can improve them and find suitable solutions in the next steps. Therefore, to catch this goal all cloud failures and their causes should be classified first. Next, all important reliability and HA measuring tools and metrics to evaluate the importance degree of each cloud components in the proposed architecture will be specified. Some main failure modes of CCSs have been proposed in [ 36 , 63 , 64 ] but as one of the future works of this research, different viewpoints exist for proposing a comprehensive classification of cloud failures. Table  4 presents a summary of these studies. Six main failure modes include software failures, hardware failures, cloud management system failures, security failures, environment failures and human faults.

Proposed roadmap of the “Which” step

One of the important aspects of software failures is database failure and data resource missing [ 36 ]. Therefore one of the HA and reliability issues in cloud computing systems will be based on the user requests to unavailable or removed data resources. For providing HA and reliability for data related services we can use 80/20 rule based on the fact that most of the data requests only access a small part of the data [ 55 ]. So, concentrating on hotspot data which are normally less than 20% of the whole data resource in terms of 80/20 rule for improving system’s availability and reliability could be one of the future approaches of this research.

Hardware failure is another important failure mode in cloud computing datacenters. In [ 65 ], hardware failures of multiple datacenters have been examined for determining explicit failure rates for different components including disks, CPUs, memory, and RAID controllers. One of the most important results of this study is that disk failure is the major source of failures in such datacenters [ 65 ]. Therefore, cloud storage devices or storage controllers as pointed in the proposed architecture can be one of the main components that can affect system reliability. Based on the conclusion of [ 65 ], we propose the hypothesis that failures of components follow the Pareto principle in cloud computing datacenters. This hypothesis can be studied as another future work of this paper. Therefore it can be said that about 80% of cloud system failures are related to storage failures.

Some researches conducted about the networks of CCS show that most of the datacenter networks and switches are highly reliable and only load balancers most often experience faults due to software failures [ 26 , 70 ].

Cloud management system (CMS) is the last important failure classification of Table  4 which will be considered in this section. Cloud management system can be considered as the manager of cloud computing services. A cloud management system uses the combination of software and technologies for handling the cloud environments. In other words, CMS can be considered as a response to the management challenges of cloud computing. At least, a CMS should be able to:

Manage a pool of heterogeneous resources.

Provide remote access for end users.

Monitor system security.

Manage resource allocation policies.

Manage tracking of resource usage.

As aforementioned in [ 71 ], three different types of failures have already been identified for CMSs and include: (1) technological failures that result from hardware problems, (2) hardware limitations creating management errors as a result of limited capability and capacity for processing information and (3) middleware limitations for exchanging information between systems as a result of various technologies using different information and data models. Currently, CMSs can detect the failures and follow the pre-defined procedures to re-establish the communications infrastructure [ 71 ]. However, the most important type of failure is related to content and semantic issues. These failures will occur when management systems operate with incorrect information, when data is changed erroneously after a translation or conversion process, or when data is misinterpreted. So, this kind of problem is still largely unsolved and is a serious problem.

One of the fundamental reasons behind avoiding the adoption and utilization of cloud services is the issue of security. Security is often considered as the main requirement for hosting critical applications in public clouds. Security failure which is one of the most important modes of cloud computing failures can be divided into three general modes which include: customer faults, software security breaches and security policy failure.

According to the Gartner predictions for IT world and users for 2016 and beyond, through 2020, 95% of cloud computing security failures will occur because of customers’ faults [ 68 ]. So, it can be said that customers’ faults will be the most important reason of cloud security failures. In addition, cloud-based software security breach is another serious issue. When cloud services are unavailable because of cloud system failures, this can cause huge problems for users who depend on their daily cloud-based activities, loss of revenue, clients and reputation for businesses. The reported software security breaches in recent years such as Adobe’s security breaches, resulted in service unavailability and cloud system failures [ 69 ].

Design and human-made interaction faults currently dominate as one of the numerous sources of failures in cloud computing environments [ 67 ]. In other words, an external fault can be considered as an improper interaction with system during the operational time by an operator. But because environmental failures and human operational faults are considered as external faults from the system viewpoint, they are beyond the scope of this paper. Based on the previous discussion it can be concluded that hardware component failures and database failures are two most important hotspots for more future studies in this step. Likewise, the most impressive factors among all cloud components can be specified as a new failure classification in future work of this research step. In addition, some reliability and availability measuring tools that would enable us to identify the most effective components in the cloud systems’ availability and reliability will be used. So the second part of this step for answering “which?” question involves identifying and classifying these measures. In the rest of this section, some of these measures will be introduced.

• Recurring faults

As earlier discussed, another goal of this part is to classify all-important reliability and HA evaluation measures. The study in [ 72 ] proposes this point that when a PC component fails, it is much more likely to fail again. This is called recurring faults which can be helpful after identifying faulty components in this step.

•Reliability importance (RI)

Identifying the weaknesses of a complex system is not as easy as identifying these weak components in a simple system such as series systems. In the complex systems, the analyst requires a mathematical approach that will provide the means of identifying the importance of each system component. Reliability Importance (RI) is a suitable measure to identify the relative importance of each component according to the total reliability of the system. The reliability importance, IR i , of component i in a system of n components is given as [ 73 ]:

where R s ( t ): is the system reliability at a certain time, t; R i ( t ): is the component reliability at a certain time, t.

The RI measures the rate of change at the certain time t of the system reliability regarding the components reliability change. The probability that a component can cause system failure at time t, can also be measured by the RI. Both reliability and current position of a system component can affect the calculated reliability importance in Eq.  1 .

So, in this step, the most important components which can have a high impact on cloud computing reliability will be identified. Therefore this step focuses on reliability issues as the first concern.

“When reliability and HA will decrease in cloud computing environments?”

Concentration will be on evaluating the different system states by answering the “When” question from the HA and reliability points of view to identify the causes and times of availability and reliability degradation at this step. Figure  5 shows the proposed roadmap of this step.

Proposed roadmap of the “When” step

Availability and reliability models capture failure and repair behavior of systems and their components. Model-based evaluation methods can be one of the discrete-event simulation models, analytic models or hybrid models using both simulation and analytic parts. An analytic model is made up of a set of equations describing the system behavior. The evaluation measures can be obtained by solving these equations. Actually, analytical models are mathematical models that present an abstraction from the real world system in relation to only the system behaviors and characteristics of interest.

This section introduces the model types used for evaluating the availability and reliability of a system. These types can be classified into these three classes:

Combinatorial model types

The three most common combinatorial model types which can be used for availability and reliability modeling under certain assumptions are “Fault Tree”, “Reliability Block Diagram” and “Reliability Graph”. These three models consider the structural relationships amongst the system components for analytical/numerical validations.

• Reliability block diagram

The reliability block diagram (RBD) is a type of inductive method which can be used for large and complex systems reliability and availability analysis. The series/parallel configuration of RBDs assist in modeling the logical interactions of complex system components failures [ 74 ]. RBDs can also be used to evaluate the dependability, availability and reliability of complex and large scale systems. In other words, it can be concluded that RBDs represent the logical structure of a system with respect to how the reliability of each component can affect the entire system reliability. In the RBD model, components can be organized into three: “Series”, “Parallel” or “k-out-of-n” configurations. In addition, these combinations together can be used in a single block diagram. Each component with the same type that appears more than once in the RBD is assumed to be a copy with independent and identical failure distribution. Every component has a failure probability, a failure rate, a failure distribution function or unavailability attached to it. A study of all existing RBD based reliability analysis techniques has been proposed by [ 74 ].

• Reliability graphs

The reliability graph is a schematic way of evaluating the availability and reliability. Generally, a reliability graph model consists of a set of nodes and edges, where the edges (arcs) represent components that have failure distributions. There is one node without any incoming edges in a reliability graph and is called the reliability graph source. In addition, there is another node without any outgoing edges and is called the sink or terminal or destination node. When there is no path from the source to the sink in a reliability graph model of a system, it is considered as a system failure. The edges can have failure probabilities, failure rates or distribution functions same as the RBDs.

• Fault tree

Fault trees are another type of combinatorial models widely used for assessing the reliability of complex systems through qualitative or quantitative analysis [ 75 ]. Fault trees can represent all the sequences of components failures that cause the entire system failure and stop system functioning, in a treelike structure. This method visualizes the cause of failure which can lead to the top event at different levels of detail up to basic events. Fault Tree Analysis (FTA) is a common probabilistic risk assessment technique that enables investigation of the safety and reliability of systems [ 76 ].

The root of a fault tree can simply be defined as a single and well-defined undesirable event. In the reliability and availability modeling, this undesirable event is the system failure. To evaluate the safety of the system, the potentially hazardous or unsafe condition is considered as an undesirable event. The fault tree is a schematic representation of a combination of events that can lead to the occurrence of a fault in the system.

State-space models

The models discussed in the previous section will be solved by using algorithms which assume that there is a stochastic independence interaction between different system components. Therefore, for availability or reliability modeling, it is assumed that the component failure or repair was not affected by other failures in the system. Therefore, to be able to model more complicated system interactions, other types of availability and reliability models such as state space models should be used.

State space models constitute a powerful method for capturing dependencies amongst system components. In other words, this model is a general approach for availability and reliability modeling which is a collection of stochastic variables which show the state of the system at any arbitrary time. The Markov chain is an example of this model type. A Markov model can be defined as a stochastic model which is used in the modeling of complex systems. It is assumed that future states in a Markov model only depend on the current state of the system. In other words, it is independent of the previous events. There are usually four known Markov models which can be used in different situations. The Markov chain is the simplest type.

A simple type of stochastic process which has the Markov properties can be considered as a Markov chain. The “Markov F Chain” term is used to refer to the sequence of the stochastic variables in this definition.

Reliability/availability modeling of any system may produce less precise results. The use of graceful degradation will make it possible for a system to provide its services at a reduced level in the existence of failures. The Markov reward model (MRM) is the usual method of modeling gracefully degradable systems [ 77 , 78 , 79 , 80 ]. In addition, the other common model types are: (1) Markov reward model or irreducible semi-Markov reward model and (2) stochastic reward nets.

Non-state-space models can help to achieve efficiency in specifying and solving the reliability evaluation, but these models assume that components are completely independent. For example, in RBDs, fault-tree or reliability graph, the components are considered as some completely independent entities in a system in terms of failure and repair behavior. It means these models assume that a component failure in a system cannot affect the function of another component. However, Markov models have the ability to model systems that reject the assumptions made by the non-state-space models but at the cost of the state space explosion.

Hierarchical models

Hierarchical models can assist in solving the state space explosion problem. They can allow the combination of random variables that show different system parameters to have a general distribution [ 81 ]. Therefore, by this approach, we can obtain more realistic reliability/availability models to analyze the complex systems. In other words, large-scale models can be avoided using hierarchical model composition. If the storage issue can be solved, the specification problem will be solved by using briefer model specifications which are transformable to the Markov models.

As a kind of summary for this section, we can express that in the context of availability and reliability modeling, many of the initial works focus on the use of Markov chains [ 82 , 83 , 84 , 85 , 86 ], because a cloud computing system is effectively considered as a complex availability problem. Other studies concentrate on the conceptual problems, priority and hierarchical graphs or the development of performance indices [ 87 ]. As stated earlier, combinatorial model types like RBDs consider the structural relationships amongst the system components which can represent the logical structure of a system with respect to how the reliability of its components affects the system’s reliability. In addition, state space models like Markov chains can be used for the modeling of more complicated interactions between components. Therefore, it seems that using hierarchical hybrid models [ 88 ], for example, combining RBDs and Generalized Stochastic Petri Nets (GSPNs) [ 89 ], RBDs and an MRM [ 90 ] are more suitable for evaluating cloud-based data centers’ availability and reliability.

To have a better understanding of reliability/availability models, the relationship between availability and reliability and their relationship with maintainability for determining the degradation states in details need to be considered, like the study proposed by [ 91 ]. Table  5 shows a summary of this study on these relationships.

Availability is defined as the probability that the system is operating properly at a given time t and when it is accessed for use. In other words, availability is defined as the probability that a system is functional and up at a specified time. At first, it would seem that if a system has a high availability, then it is also expected to have a high reliability. However, this is not necessarily true [ 91 ], as shown in Table  5 .

Reliability shows the probability of a system/component for performing the required functions in a period of time without failure. It does not contain any repair process in its definition. It also accounts for the period of time that it will take the system/component to fail during its operating time. Therefore, according to the definition of availability, it can be said that availability is not only a function of reliability, but it is also a function of maintainability. Table  5 shows the reliability, maintainability, and availability relationships. From this table, it should be noted that an increase in maintainability means a decrease in the repair time and maintenance actions period. As shown in Table  5 , if the reliability can be held constant, even at a high value, this does not directly mean providing high availability, because as the time to repair increases, the availability will decrease. Therefore, even a system with a low reliability could have a high availability provided that the repair time does not take too long.

Finally, for answering the ‘When’ question is to have an appropriate definition of availability. The definition of availability can be flexible, depending on the types of downtimes and actor’s points of view considered in the availability analysis. Therefore, different definition and classifications of availability can be offered. A classification of availability types has been proposed by [ 91 , 92 ]. In the following, four common types of availability will be introduced briefly:

• Instantaneous availability

Instantaneous availability is defined as the probability that a system is operating at any given time. This definition is somehow similar to the reliability function definition which gives the probability that a system will function at the given time t. However, different from the reliability definition, the instantaneous availability contains information on maintainability. According to the instantaneous availability, the system will be operational provided the following conditions could be met:

The required function can be properly provided during time t with probability R(t);

It can provide the required function since the last repair time like u, 0 < u < t, with the following probability:

where m (u) is the system renewal density function.

The instantaneous availability is calculated as:

• Mean availability

The average uptime availability or mean availability can be defined as the proportion of mission time or time period that a system is available for use. The mean value of the instantaneous availability over a specific period of time such as (0, T), is defined by this availability.

• Steady-state availability

The steady state availability can be determined by calculating the limit of the instantaneous availability as the time goes to infinity. In fact, when the time approximates to about four times the MTBF in instantaneous availability, it can be said that the instantaneous availability function approaches the steady state value. Therefore, the steady state availability can be calculated by using the following equation:

• Operational availability

Operational availability can be considered as an important measurement for evaluating the system effectiveness and performance. It evaluates the system availability which includes all the downtime sources, such as diagnostic downtime, administrative downtime, logistic downtime, etc. The operational availability is calculated as:

where the operating cycle is the overall time of the investigated operation period whereas Uptime is the system’s total functional time during the operating cycle. It is important to note that the availability that a customer actually experiences in the system is operational availability which is the availability according to the events that happened to the system.

“How to provide high availability and reliability while preventing performance degradation or supporting graceful degradation?”

As the last important step that should be taken, suitable solutions will be chosen to provide HA and reliability, while supporting graceful degradation based on the previous results as shown in Fig.  6 . Table  6 presents fault tolerance (FT) methods classification in cloud computing environments.

Proposed roadmap of the “How” step

By identifying the states which have higher failure rate and specifying the causes based on the results obtained from “Where?”, “Which?” and “When?” steps, we will be able to provide high available and reliable services in our cloud computing systems. Therefore, appropriate action can be taken according to the specific occurrence and failure type because there is no one-size-fits-all solution in the HA and reliability issues area.

Fault tolerance mechanisms deal with quick repairing and replacement of faulty components for retaining the system. FT in cloud computing systems is the ability to withstand the abrupt changes which occur due to different types of failures. Recovery point objective (RPO) and recovery time objective (RTO) are two major and important parameters in fault management study. The RPO shows the amount of data to be lost as a result of a fault or disaster, whereas RTO shows the minimum downtime for recovering from faults [ 93 ].

Ganesh et al. [ 93 ] presented a study on fault tolerance mechanisms in cloud computing environments. As noted in this paper, there are mainly two standard FT policies available for running real-time applications in the cloud; they are “Proactive Fault Tolerance Policy” and “Reactive Fault Tolerant Policy”. These FT policies are mainly used to provide fault tolerance mechanisms in the cloud.

The proactive fault tolerance policy is used to avoid failures by proactively taking preventive measures [ 95 , 96 , 97 ]. These measures are reserved by studying the pre-fault indicators and predicting the underlying faults. The next step is applying proactive fault tolerance measures by refactoring the code or failure prone components replacement at the development time. Using proactive fault tolerance mechanisms can guarantee that a job execution will be completed without any further reconfiguration [ 98 ].

The principle of reactive fault tolerance policy is based on dealing with measures applied for reducing the effects of the faults that already occurred in the cloud system. Table  6 shows a summary of this study on FT policies and techniques in cloud computing systems.

Although cloud computing systems have improved significantly over the past few years, improvements in computing power depend on system scale and complexity. Modern processing elements are extremely reliable, but they are not perfect [ 99 ]. The overall failure rate of high-performance computing (HPC) systems such as cloud systems, increases with their size [ 99 ]. Therefore, fault tolerating methods are important for systems with high failure rate.

Proactive FT policies in cooperation with failure prediction mechanisms can avoid failures by taking proactive actions. The recovery process overheads can be significantly reduced by using this technology [ 99 ]. Task migration is one of the widely used techniques in Proactive FT techniques. While task migration can avoid failures and achieve much lower overheads than Reactive FT techniques like Checkpointing/Restart, there are two shortcomings in this technique. First, migration will not be performed if there is no spare node for accommodating the processes of the suspect node. The second problem is that task migration is not an appropriate method for software errors [ 100 ]. Thus, a proactive fault tolerance method which only rely on migration cannot gain all advantages of the failure prediction.

One of the other techniques to provide fault tolerance in cloud environments is by using VM migration. Jung et al. [ 101 ] propose a VM migration scheme using Checkpointing to solve the task waiting time problem. A fault tolerant mechanism usually triggers the VM migration before a failure event. It actually backs VM to the same physical server after system maintenance ends [ 102 ].

Redundancy is a common technique for providing high available and reliable systems [ 103 ], but it could not be as effective as expected as a result of particular types of failures such as transient failures and software aging. The most common procedure for combating software aging is to apply software rejuvenation. A software rejuvenation process can be done by restarting the software application. Therefore, this technique will help to restart the application to its standard level of performance and effectively solve the software aging problem. In other words, software rejuvenation is a preventive and proactive technique which is particularly useful for counteracting the appearance of software aging, aimed at cleaning up the internal states of the system to prevent further and future failure events. Without any rejuvenation, both the OS software and the hosted running application software will be degraded in performance with time due to the exhaustion of system resources such as free memory that could finally lead to a system crash, which is very undesirable to HA systems and fatal to mission-critical applications. The disadvantage of rejuvenation is the temporary outage of service. To completely eliminate the service outage during the rejuvenation process, the use of virtualization technology is one of the possible approaches [ 104 ].

The software rejuvenation process can be applied to the system according to the different parameters of software aging or the elapsed time since the last rejuvenation event. Software rejuvenation can be used at different scopes like system, application, process, or thread [ 105 ].

A classification of techniques has been proposed by [ 106 ] which distinguishes between two classes of failure handling techniques, namely, task level failure handling and workflow level failure handling. The recovery techniques that can be performed at the task level for masking the fault effects are called task level techniques. These types of recovery techniques have been widely studied in distributed and parallel environments. These recovery techniques are classified into four different types: retry, alternate resource, checkpoint/restart, and replication techniques. Generally, it can be concluded that the two simplest task level techniques are retry and alternate, as they simply try to execute the same task on the same or alternate resource again after failure.

The checkpoint is mostly efficient for long-running applications. The checkpointing approach has attracted significant attention over the recent years in the context of fault tolerance research like studies that have been carried out by [ 107 , 108 , 109 , 110 ]. Generally, the checkpoint technique periodically saves the state of an application. After the checkpoint, failed tasks can restart from the failure point by moving the task to another resource. There are several different checkpointing solutions for large-scale distributed computing systems. A classification of checkpointing mechanisms have been proposed in [ 111 ]. It shows that these mechanisms can be classified based on four different viewpoints. From one viewpoint, the checkpointing mechanism can be classified into two groups called the incremental checkpointing mechanisms and the full checkpointing mechanisms, depending on whether the newly modified page states are saved or the entire system running states are stored. The second viewpoint classifies the checkpointing mechanisms into two local and global checkpointing mechanisms, according to how checkpointing data is saved, locally or globally. Depending on whether the checkpointing data is saved coordinately or not, the third classification can be proposed, which is organized into two coordinated checkpointing mechanisms and uncoordinated checkpointing mechanisms. Finally, it can be concluded that the last classification is based on saving the checkpointing data on disk or not, and they are called disk and diskless checkpointing mechanisms.

Finally, as the last task level technique, we point to the replication. Running the same task on different computing resources and simultaneously ensures that there is at least one successful completed task which is the replication technique [ 112 , 113 ].

As previously mentioned, workflow-level techniques are the second class of failures handling in distributed systems. Manipulating workflow structure for dealing with erroneous conditions is referred as workflow level technique. In other words, workflow level FTTs change the flow of execution on failure according to the knowledge of task execution context. They can also be classified into four different types: alternate task, redundancy, user defined exception handling and rescue workflow [ 106 ]. The only difference between alternate task and retry technique is that alternate task exchanges a task with a different implementation of the same task with different execution characteristics on the failure of the first one. The rescue workflow technique allows the workflow to continue even when task failure occurs. In another technique, the particular treatment to the task workflow is specified by the user and it is called user defined exception handling technique.

As a conclusion of the previous discussion, it can be said that both proactive and reactive fault tolerance policies have advantages and disadvantages. The results of the experiment show that migration techniques (proactive FT policy) are more efficient than checkpoint/restart techniques (reactive FT policy) [ 98 ]. Even though proactive techniques are more efficient, they are not frequently used. This is because the system is less affected by incorrect predictions due to proactive fault tolerance and also reactive methods are relatively simple to implement as FT techniques are not applied during the development time.

There is a serious gap in cloud computing fault tolerance research area, that is, the mutual impact of cloud performance overhead [ 52 , 114 , 115 , 116 , 117 ] and HA solutions.

There are hot topics like VM Granularity, IaaS Routing Operations (such as live VM migration, concurrent deployment and snapshotting of multiple VMs …), and the inability to isolate shared cloud network and storage resources in the cloud performance overhead research area. All these factors can impact on VM performance, total system performance and therefore VM’s availability and reliability. On the other hand, using additional mechanisms for providing HA and reliability in cloud computing systems can be considered as a system overhead which can have negative effects on system performance.

It seems that interesting and desired results would be achieved as the contribution of this research step by studying these mutual impacts for providing highly available and utilized cloud computing systems and considering performability concerns.

Discussion and open issues

Many research works have been carried out on cloud computing HA and reliability, but there is no comprehensive and complete overview of the entire problem domain. Attempt was made to propose a reference roadmap that covers all the aspects of the problem from different cloud actor’s viewpoints, especially cloud consumers and providers. This study proposed a big picture which divided the problem space into four main steps to cover the various requirements in the desired research area. A specific question was posed for each step that answering these questions will enable cloud providers to offer high available and reliable services. Therefore, while cloud providers can satisfy the cloud consumers’ requirements, they can also have highly utilized resources to achieve more business profits.

The four major questions starting with “Where?”, “Which?”, “When?” and “How” keywords form the main steps of the proposed roadmap. By taking these steps, our purposes can be achieved. In fact, taking each step means understanding the main concern presented by the specific question and proposing suitable solutions for that issue. Some suggestions were proposed as the primary answers for each of these questions which can be helpful for more future work in this research area. Making future research suggestions and proposing efficient solutions for each of these steps in the big picture is one of the important open issues in this study. Therefore, the proposed big picture can lead to future researches in cloud computing in the field of high availability and reliability.

Two main research gaps were specified through the proposed big picture, which have been neglected in the literature review. The first one is related to the providing HA and reliability solutions regardless of considering all cloud actors’ requirements and viewpoints. By proposing “Where” question, attempt was made to consider both cloud consumers and providers which are most important actors in the cloud computing environments. Therefore, not only was attention given to have a high available and reliable system, but also provider’s demands for having highly utilized systems were considered. The second research gap was proposed in “How” question section. As pointed out, adding an additional mechanism to the system can have negative effects on the total system performance. In addition, some performance overhead issues can degrade system availability and reliability. Therefore, this scientific hypothesis that system performance overhead and HA solutions can have the mutual impact on one another was proposed.

This paper presented a reference roadmap of HA and reliability problem in cloud computing systems that can lead to future research papers. By proposing this roadmap, all the different aspects of HA and reliability problem in cloud computing systems were specified. Furthermore, the effects of cloud computing main components on total system failure rate were studied according to the proposed eucalyptus-based architecture in our big picture. In this study, we focus on the comprehensive roadmap and covering all the different related aspects of HA and reliability in cloud environments. In addition, attempt was made to consider not only techniques (‘When’ and ‘How’ steps) that improve availability and reliability, but also characteristics (‘Where’ and ‘Which’ steps) of cloud computing systems. However, we will go into the details of specific technologies for each step as the future work of this study. Therefore, we can assess each step of the proposed solution in a more specific way. Evaluating the mutual impact of system performance overhead and HA solutions using the OpenStack platform is one of the main future works. SDN-based technology such as Espresso in Google cloud is one of the latest technology trends in cloud computing that can make cloud computing environments faster and more available. The application of this approach will be considered as a solution to ‘How’ step in future work. Furthermore, the contributions in this paper raise some other future works of reliability and HA in cloud computing through interesting proposed aspects of the problem. Some other significant future works are as follows:

A comprehensive study of cloud failure modes, causes and failure rate, and reliability/availability measuring tools;

A highly utilized and more profitable cloud economy that can guarantee the provision of highly available and reliable services.

Evaluating availability and reliability of cloud computing system and components based on the proposed architecture;

Studying the mutual impact of HA mechanisms and VM performance overhead.

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Mesbahi, M.R., Rahmani, A.M. & Hosseinzadeh, M. Reliability and high availability in cloud computing environments: a reference roadmap. Hum. Cent. Comput. Inf. Sci. 8 , 20 (2018). https://doi.org/10.1186/s13673-018-0143-8

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Cloud computing: state-of-the-art and research challenges

• Qi Zhang 1 ,
• Lu Cheng 1 &
• Raouf Boutaba 1  

Journal of Internet Services and Applications volume  1 ,  pages 7–18 ( 2010 ) Cite this article

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Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.

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Zhang, Q., Cheng, L. & Boutaba, R. Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1 , 7–18 (2010). https://doi.org/10.1007/s13174-010-0007-6

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Survey on serverless computing

• Hassan B. Hassan 1 ,
• Saman A. Barakat 2 &
• Qusay I. Sarhan 2  

Journal of Cloud Computing volume  10 , Article number:  39 ( 2021 ) Cite this article

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Serverless computing has gained importance over the last decade as an exciting new field, owing to its large influence in reducing costs, decreasing latency, improving scalability, and eliminating server-side management, to name a few. However, to date there is a lack of in-depth survey that would help developers and researchers better understand the significance of serverless computing in different contexts. Thus, it is essential to present research evidence that has been published in this area. In this systematic survey, 275 research papers that examined serverless computing from well-known literature databases were extensively reviewed to extract useful data. Then, the obtained data were analyzed to answer several research questions regarding state-of-the-art contributions of serverless computing, its concepts, its platforms, its usage, etc. We moreover discuss the challenges that serverless computing faces nowadays and how future research could enable its implementation and usage.

Introduction

Cloud computing emerged after the appearance of virtualization in software and hardware infrastructures; hence cloud providers increasingly adopted it to offer their services to customers [ 1 , 2 ]. Customers can access these cloud services via the Internet. Software developers have been using cloud technologies in their software solutions owing to their benefits including scalability, availability, and flexibility [ 3 ].

In general, cloud computing is divided into three main categories based on the provision of services, which are software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). In the SaaS category, cloud providers offer different types of software as services to the users. For example, Google provides many applications as a service (e.g., Gmail, Google docs, Google sheets, and Google forms). In this type of cloud, the user is not responsible for the services development, deployment, and management. The user here only uses them without worrying about their settings, configurations, etc. Meanwhile, in the PaaS, cloud companies provide services such as network access, storage, servers, and operating systems to be purchased by developers. The developers access these services to deploy, run, and manage their applications. In this kind of cloud, the developer is responsible for the deployment and management (settings and configurations) of their software to ensure that the application is running, while they do not control the services. Finally, in the IaaS category, the cloud consumers control and manage services such as network access, servers, operating systems, and storage.

Managing cloud services is not an easy task at all. The authors in [ 4 ] have addressed several challenges while managing a cloud environment by a user such as availability, load balancing, auto-scaling, security, monitoring, etc. For example, the cloud user has to ensure the availability of the services in which if a single machine failure occurs, it does not affect the whole services. Also, he/she has to consider distributing copies of the services geographically to protect them when disasters happen. Another challenge is load balancing. In this case, the cloud user has to ensure that requests to the services are balanced to utilize all resources efficiently.

These challenges have led to introduce another cloud computing model, which is called serverless cloud computing [ 4 ]. Serverless cloud computing offers backend as a service (BaaS) and function as a service (FaaS), as shown in Fig.  1 . The BaaS includes services like storage, messaging, user management, etc. While, the FaaS enables developers to deploy and execute their code on computing platforms. The FaaS relies on the services provided by the BaaS such as a database, messaging, user authentications, etc. The FaaS is considered as the most dominant model of serverless, and it is also known as “event-driven functions” [ 5 , 6 ].

Serverless architecture

Serverless cloud model was for the first time introduced by Amazon Lambda in 2014, after which cloud companies like Google and Microsoft adopted it in 2016. Serverless cloud computing adds an additional abstraction layer to the existing cloud computing paradigms, while it abstracts away the server-side management from the developers [ 7 ]. Serverless model lets the developers focus on the application logic rather than the server-side management and configurations. For example, the developers deploy their applications to the serverless cloud as functions see Fig.  1 . Then, the cloud provider takes responsibility for managing, scaling, and providing different resources to ensure the smooth running of these functions [ 8 , 9 ].

However, FaaS and the term “serverless” could be used interchangeably, as the FaaS platform automatically configures and maintains the execution context of functions and connects them to cloud services without requiring server provision by developers [ 10 , 11 ]. We refer to the FaaS when we use the term serverless computing.

Serverless cloud computing has many good characteristics [ 12 , 13 ], one of which is scalability. Scaling could be vertical or horizontal; vertical scaling adds or removes cores from the running container, while horizontal scaling creates new containers or eliminates running ones without affecting the current resource allocations [ 14 ]. In serverless computing, the applications automatically scale up and down on demand, and the developer does not have to concern themselves about the scaling issues. For example, when an application runs on a serverless cloud, it will scale up automatically when the application requests increase. Another characteristic of serverless computing is the payment per resource usage. This paradigm of cloud computing charges developers based on the actual resource usage. For example, deploying an application will not cost the developer in the case where the application is idle, and the serverless provider will only charge whenever the application has started using resources.

However, any new technology will face numerous technical and operational issues and obstacles at the beginning. Since the recent introduction of serverless cloud computing, several drawbacks have been identified [ 7 ]. Serverless cloud computing lacks tools that help managing and monitoring serverless applications. Moreover, it might comprise security concerns. Further, the serverless providers have a vendor lock-in problem. Nevertheless, serverless cloud computing has gained positive attention in the industry, despite that it has not been studied extensively in academic research [ 7 ].

Therefore, the aim of this research is to answer some crucial research questions related to serverless cloud computing and thereby help researchers as well as developers to better understand serverless cloud computing and contribute to its development.

The rest of this paper is structured as follows: “ Related works ” section presents the related works for this study. “ Research methodology ” section describes in detail the research methodology used to conduct this survey study. “ Results ” section presents the results and outcomes of the study. “ Threats to validity ” section presents the threats to validity of this study. Finally, the conclusions of the study are provided in “ Conclusions ” section.

Related works

The most relevant studies published on the topic are briefly presented here. The authors in [ 15 ] and [ 16 ] discussed some important background to the origin and evolution of serverless computing and the long road that serverless computing has taken over the years. The authors in [ 9 ] thoroughly discussed the true meaning of serverless architectures and how they are changing the way in which applications are built, deployed, and distributed.

Numerous studies focused on technical interpretations of serverless computing, while other more recent research suggested various benefits that it brings to developers. Nowadays, this type of computing is being used in several ways. In an empirical study, the authors in [ 17 ] aimed to investigate the development practices of serverless computing in the industry. They concluded that for developers, it remains a barrier to adopt the right mindset to best utilize the tools inherent to serverless architecture. More documentation and easier access to such resources would help developers to embrace the possibilities that serverless computing has to offer.

The concept of serverless computing within the scope of the IT industry has the great potential of progressively increasing its capabilities to involve a wider set of domains. Thus, the implementation of serverless computing is not restricted only to the enhancement of infrastructure, and it can be employed for many different purposes, e.g., serverless messaging, neural network training [ 18 ], video processing [ 19 ], and big data [ 20 ]. Undeniably, their contributions are valuable to the general public and researchers in the field, as it is of primarily importance to comprehend how this technology works.

However, it is presently crucial to provide more than only theories and concepts: it is time to weigh the benefits and drawbacks of serverless computing and to analyze how far the field has progressed, to assess what remains to be done and improved. As an example, the authors in [ 21 ] discussed some possible new abstraction levels to reduce processing limitations. The authors in [ 22 ] discussed the results from an open-source framework to achieve on-premises serverless computing that can process big workloads with a scalable and sensible usage of resources. We can infer from these related publications that researchers everywhere are working to determine how to best exploit the potential that serverless computing frameworks could introduce to software development.

In [ 23 ], the authors described how serverless computing is becoming the next step in the evolution of cloud computing and its platforms. In our paper, we focus on the ongoing challenges, benefits, and drawbacks of using it.

The authors in [ 24 ] have conducted a systematic exploration of serverless computing-related research papers. As they mentioned, their work is not a survey, but it is a supporting source for future research papers. They proposed an open dataset for serverless computing papers. The dataset includes 60 papers for the period (2016-July 2018). Also, they have analyzed the dataset according to bibliometric, content, technology, and produced statistics about each section. In contrast, our paper aims to conduct a systematic survey. In this survey, we try to find answers to several critical questions related to serverless computing. In addition to that, our study covered the duration (2016–2020) and thus 275 papers have been considered.

The authors in [ 25 ] mainly focused on scheduling tasks in the cloud. They described the various techniques in scheduling workflows to reduce the execution time, cost, or both. Moreover, they proposed a hybrid method by both FaaS and IaaS. The small tasks could be executed remotely using the FaaS, which reduces the execution cost; hence, the number of virtual machines will be decreased as well. Therefore, the whole focus would be on the long-running tasks on IaaS.

The authors in [ 26 ] covered only 24 research papers during 2017–2019. In their paper, they considered both the white and grey literatures. Besides, they identified 32 characteristics of serverless and the possible issues related to them, only eight of them were stated by both white and grey literatures while the remaining are from grey literature only. All the characteristics are explained and presented briefly. In our paper, 275 research papers from 2016–2020 have been covered and more research questions have been answered. Besides, a well-defined systematic literature study process has been employed. Thus, the grey literature has been excluded in our paper and, our results are reproducible compared to their results.

The authors in [ 27 ] mainly concentrated on difficulties and gaps in data-centric and distributed computing using FaaS. Additionally, they evaluated the severity of these challenges via taking three case studies from big data and distributed computing settings: model training, low-latency prediction serving using the batch and, distributed computing. While our paper is a broad and comprehensive study on FaaS, 275 research papers are taken from the white literature during 2016–2020.

The paper [ 28 ] presented only four use cases of FaaS: event-triggered computing, video broadcasting, Internet of Things (IoT) data processing, and shared delivery system. Additionally, the paper only compared three platforms namely, Amazon web services (AWS) Lambda, Google Cloud Function, and Microsoft Azure Function. On the other hand, our paper presents a comprehensive study about FaaS. We identified in detail eight use cases: chatbot, information retrieval, file processing, smart grid, security, networks and, mobile and IoT. Moreover, our paper compared ten FaaS platforms namely, AWS Lambda, Apache OpenWhisk, Microsoft Azure functions, Google Cloud functions, OpenLambda, IBM Cloud functions, OpenFaaS, Knative, FunctionStage, Huawei Cloud, and Nuclio.

The authors in [ 29 ] covered only 15 papers during 2016–2018. They took both the white and grey literatures into account. On the other hand, our paper includes 275 research papers published in the period 2016–2020; they are taken from the white literature only. Moreover, our paper has formulated and answered eight clear and well-defined research questions.

The authors in [ 30 ] focused on the FaaS performance evaluation and their publication trends during 2016–2019. They identified the most commonly evaluated FaaS platforms. Additionally, they evaluated the performance features for benchmark types, micro-benchmarks, and common features across FaaS platforms. Moreover, they presented the most common platform configurations in FaaS, namely language runtimes, function triggers, and external services. This paper presents a survey of the most important and state of the art aspects of FaaS. Besides, comprehensive theoretical aspects of FaaS are covered taking from the white literature during 2016–2020.

The authors in [ 11 ] have conducted a systematic mapping study on serverless cloud computing. The main aim of their study is to concentrate on FaaS engineering. They raised two main concerns: (a) identifying publication research that considers developing or modifying serverless platforms and tools. (b) identifying the challenges and drivers related to these publications. On the other hand, our study extends the challenges and issues related to serverless computing. Moreover, we provide more details about serverless computing platforms and the use of these platforms in the literature. Also, it provides a detailed comparison among the most widely used serverless platforms. Besides, it addresses more aspects of serverless cloud computing such as application areas of serverless computing, future directions of serverless computing, etc.

The authors in [ 7 ] provided useful observations about serverless computing, its architecture, and use cases. Also, they discussed the challenges and benefits of moving forward from monolithic applications and the differences between traditional cloud services and serverless computing. Our work has extended the details of their work regarding the benefits and drawbacks of using serverless computing. It has also included more use cases and workloads to deepen the findings of previous studies.

The authors in [ 4 ] presented a technical report on serverless computing. They covered the serverless emergence with its limitations, including limited storage for fine-grained tasks, lack of coordination among functions, inadequate performance for standard communication patterns, and functions’ performance. Also, they compared AWS serverful with AWS serverless. Moreover, they also explained the challenges of architecture, networking, security, and abstractions of serverless computing. They identified five application areas including, video encoding in real-time, MapReduce, linear algebra, machine learning training, and databases. While our paper has covered 275 research papers from 2016–2020 forming a well-defined systematic literature study. We also identified 21 serverless challenges and issues. Besides, we compared serverless with the traditional cloud computing paradigm. We identified more application areas including, chatbot, information retrieval, file processing, smart grid, security, networks, IoT, and edge computing.

The authors in [ 31 ] presented a white paper based on published research papers during 2015–2017. They outlined the serverless definition alongside its advantages and disadvantages. Also, they classified serverless use-cases into six categories, namely, backends, web applications, chatbots, big data, IT automation, and Amazon Alexa. Moreover, they addressed a few research questions including, the need for the stateless feature in serverless, whether serverless could execute long-running tasks, programming models, serverless standards, and the importance of serverless for scientific research. While our paper is a comprehensive study on FaaS; we covered 275 research papers which are taken from the grey literature during 2016–2020. In our paper, eight application areas have been identified as mentioned earlier. We have identified and answered ten research questions that cover many aspects of the topic in detail compared to the aforementioned study.

We are in fact addressing with this paper ten important research questions about the topic, potentially making it a more complete guide to the development and use of serverless computing. Our work contributes to the analysis of the serverless paradigm in the context of similar applications and how could they better fit specific computing needs. Moreover, information about the current state of serverless platforms, tools, and frameworks has been updated for this survey. This due to the importance of the topic and its potential to change how both the industry and academia have managed the deployment of cloud applications until now. Updated information about the area could benefit future studies focused on the serverless computing paradigm as they make researchers aware of the latest resources and opportunities in the area.

Research methodology

Research questions.

In this study, a number of research questions (RQs) have been identified and answered. Each RQ addresses a particular aspect of serverless computing as follows.

RQ1. What is the number and distribution of studies published on serverless computing in the period (2016–2020)?

RQ2. Which researchers, organizations, and countries are active in serverless computing research?

RQ3. What are the differences between serverless computing and traditional cloud computing?

RQ4. What are the benefits of using serverless computing?

RQ5. What are the most used software platforms that enable serverless computing in the literature?

RQ6. What are the application areas of serverless computing in the literature?

RQ7. What are the challenges and issues of using serverless computing?

RQ8. What tools are available for serverless computing? (serverless tools)

RQ9. What are the available research approaches to analyze the migration of monolithic applications to serverless computing?

RQ10. What are the potential future directions of research on serverless computing?

Search strategy

Literature sources.

In this study, five standard online databases have been selected as sources that index the literature of software engineering and computer science. These sources are presented in Table  1 .

Search string

To find the publications relevant to this study, the following extensive search string has been applied on the database sources of literature:

(serverless OR FaaS OR “function as a service” OR “function-as-a-service”) AND (computing OR paradigm OR architecture OR model OR application OR function OR service OR platform OR programming)

To obtain the best publication list, a generic search string is created. It contains serverless cloud computing-related keywords. The string with duration (2016 - 2020) have been applied to all libraries. Because the Springer Link library covers many fields, the result of search was greater than other libraries. This because the keyword FaaS is used in many research areas for different purposes. For instance, fish as a service (FaaS) and FPGA as a fervice (FaaS). Therefore, we used Computer Science subject filter with Springer Link, ScienceDirect, and Scopus to reduce the number of incorrect papers. The results of the initial search are shown in Fig.  2 . Additionally, some inaccurate results have been obtained due to the partial similarity to FaaS, such as the federal aviation administration (FAA). The results of the initial search were 5,021 papers in total.

Results of papers selection process

After obtaining the initial list of publications, some filters have been applied to reduce the number of incorrect results based on their relation to the serverless computing and FaaS topics. Most of the papers have been analyzed based on the title and abstract. However, when we were unable to make a decision based on the title and the abstract, we read the content of the paper to ascertain whether to include or exclude. As a result, the list of papers which are related to serverless computing has been decreased to 549 papers.

After filtering the papers based on the title and abstract, we merged all the papers that were relevant to serverless cloud computing, which was 549 papers into a single dataset. Then we removed the duplicated papers based on the combination of a title, author names, publication year, and venue. Thus, the number of publications has been reduced to 489 papers.

Then, the publications have been selected based on the content of the paper and based on a set of inclusion/exclusion criteria (see the following section) that have been selected carefully. Eventually, we could obtain 254 papers that are related to serverless cloud computing. In the next step, we applied backward snowballing to increase the set of relevant papers to serverless cloud computing. In this phase, we could add 21 more papers to our list of papers. As a result, the total numbers of relevant papers become 275 papers. The list of these papers and its meta-data have been published in Zenodo website as a dataset [ 32 ].

Paper inclusion/exclusion criteria

To decide whether a publication is relevant to the scope of this research, a set of inclusion and exclusion criteria have been established and employed as follows:

Inclusion criteria:

Publications in the field of software engineering and computer science.

Publications published online from 2016 – 2020.

Publications related directly to serverless computing.

Exclusion criteria:

Publications not published in English.

Publications without accessible full text.

Publications not formally peer reviewed (e.g., gray literature).

Publications not published electronically.

Publications that are duplicates of other previous publications.

The selected publications were carefully read to answer the raised RQs. Here, a short title is used to represent each RQ. The following subsections present and discuss the results based on each RQ.

Distribution of publications (RQ1)

Publication frequency.

All the selected papers of this study were analyzed to determine their frequency and evolution. Figure  3 shows the results of this analysis. The results show that the average number of publications per year is approximately 55 papers.

Published papers per year

Serverless computing has trended a significant engagement over the past two years. This boost has been caused by industry, academia, and developers for several reasons. The first important reason is the attractive engagement opportunities that serverless offers cloud providers. Serverless nature equipped cloud providers with more convenient and efficient methods to manage and utilize idle computing resources. Another reason is that the billing is only on the basis of function execution time and resource allocation. Also, the developers are not required to be aware of the underlying infrastructure and workflows. Hence, this attracts cloud providers and businesses to migrate and support serverless alongside many directions. At the same time, researchers are paying more attention to serverless as it is becoming the future paradigm for cloud computing. Moreover, current challenges and limits in serverless computing draw attention to more academics to address the issues and enhance the currently available features. For the aforementioned reasons, developers and customers are well encouraged and satisfied to select serverless computing for developing applications and services.

Publication venue

The distribution of the selected papers in various publication venues is shown in Fig.  4 . The percentages of publications in conference papers, workshop papers, symposium papers, and journal papers are approximately 62%, 11%, 14%, and 13%, respectively. However, almost 13% of the studies have been published in journals, which indicates the immaturity of research in serverless computing [ 33 , 34 ]. It is worth mentioning that some conference papers were published as book chapters. Thus, the original venues, which are conferences, of such papers were considered.

Published papers ratio per each venue

Following the interpretation of publications, the most productive and primary journals, symposiums, conferences, and workshops venues related to serverless computing can be clarified. Due to their long names, abbreviations are used in this paper. The active journals are shown in Fig.  5 and their full names can be found in Table  2 . It can be observed from the figure that the top and vital three journals are “FGCS”, “IoT”, and “JSS”. Also, it can be noticed that the top three journals contain almost 34% of the published journal papers, while the others own approximately 66%.

Published papers vs. journal name

The active conferences are shown in Fig.  6 and their full names are presented in Table  3 . The “WOSC”, “Cloud”, “UCC”, “SoCC”, and “Middleware” are considered the most active conferences that hold approximately 28% of the published conference papers. By including other conferences with three published papers or more, then approximately 23% of the conference papers are published in annual conferences. The majority (almost 49%) of the conference papers were published at individual conferences, which are denoted as “Others” in Fig.  6 .

Published papers vs. conference name

Active researchers (RQ2)

Serverless computing is a vital research area through the contribution of several scholars. Yet, the researchers are counted active if they contributed to more than two research studies, as presented in Fig.  7 . The figure shows that the top six active researchers are “Pedro Garcáa López”, “Erwin Van Eyk”, “Alexandru Iosup”, “Marc Sánchez-Artigas”, “Sebastian Werner”, and “Wes Lloyd”. Table  4 presents the active nations, research institutions, researchers, references to the published papers, and the total number of publications.

Active researchers based on the published papers

The active nations in the number of papers are obtained from the information presented in Table  4 by extracting the institutional affiliation of the authors and co-authors. An overview of the most active nations and the total number of publications is shown in Fig.  8 . It is observable that the United States and Germany are the largest contributors to papers published on serverless computing with 104 and 39 published papers, respectively.

Active countries

Serverless computing vs. traditional cloud

computing (RQ3) There are several differences between serverless and traditional cloud computing. In the traditional cloud architecture, the server acts as a monolithic system containing all business logic. Meanwhile, the serverless architecture is modeled into smaller, event-driven, and stateless ‘triggers’ (events) and ‘actions’ (functions) [ 175 ]. Each component handles different pieces of data and runs independently [ 176 ]. Spreading business logic into smaller functions increase the development efficiency [ 77 , 177 ] and also decreases the chance of a single point of failure [ 77 ]. On the other hand, the component dependency within monolithic applications affects the availability of other services adversely.

In a serverless architecture, the developers are unable to take control of listening to the TCP socket, managing load balancers, maintenance or configuration of the server, as well as provisioning and resource allocation. Therefore, there is no need for system administrators; the developers only focus on handling client requests and paying attention to deliver valuable services [ 8 ].

Serverless computing also differs from monolithic computing as the functions have shorter life cycles.

The traditional monitoring and debugging tools that are used in monolithic applications are not included in the serverless architecture; the developers are compelled to use built-in tools for debugging and monitoring. The computing power is no longer a concern for the developers in the serverless paradigm, as it could scale horizontally almost indefinitely [ 178 , 179 ]. Meanwhile, in the client-server architecture, it usually requires dedicating two server instances; the primary instance and a second in case if the former fails. This leads to higher costs in the monolith paradigm. Serverless architecture could be more economical for unsteady load conditions while the server-based is more suitable for steady loads [ 152 ]. As serverless applications scale up and down according to the requests, thus, unlike the traditional systems, it is unnecessary to keep the sessions in the memory [ 8 ]. Hence, it is difficult to keep track across requests.

FaaS boosts the security level as cloud providers continuously update their infrastructure with the latest security patches; this also removes the security burden on developers [ 17 ]. Directly accessing the backend resources in the traditional model is considered a critical security issue. Thus, any requests from the clients and internal functions in the serverless environment must go through a distributed request-level authorization mechanism that strengthens the security level [ 8 ]. Additionally, denial of service (DoS) attacks are controlled, as it is more difficult to attack distributed servers than a single server [ 175 ]. However, some security concerns remain due to the third-party API usage [ 9 ]. Besides, there is a lack of tools to identify vulnerabilities and access control risks. Table  5 summarizes the aforementioned differences.

Benefits of serverless computing (RQ4)

Serverless computing offers numerous benefits to its users, and Table  6 presents papers that states these benefits. This section summarizes those benefits as follows:

Cost effective

Serverless applications are abstracted from server infrastructure, which results in cost-based services depending on usage [ 180 ]. For example, applications run whenever a user makes a request to a service within the application. The cloud vendors charge only for the used space, and there is no cost while their applications are in an idle state.

Scalability

Serverless reasonably solved the resource allocation problem [ 191 ]. Therefore, developers do not have to concern themselves with the application scalability, because the application will scale automatically whenever user application requests are increased. If there are numerous requests to a function within the application, the serverless providers will start servers to handle these requests.

Server-side management

In serverless computing, developers do not need to concern themselves with the server-side and its management. Serverless cloud providers take care of managing and maintaining the hardware and software required to deploy applications. In addition to that, they handle all administration operations to let developers focus on different kinds of resources such as central processing unit (CPU), memory, and storage.

Easy to deploy

Serverless applications are easy to deploy. For example, to deploy an application, developers only need to upload some functions and release a new product. The serveless will take care of deployment management and infrastructure related concerns such as server provisioning and scaling.

Decrease latency

Serverless applications are not hosted on a specific server; the code can run from any server in any location. Therefore, cloud vendors can run the application on servers near the end user’ location. This reduces latency, because end user requests do not have to travel across the Internet to access the original server.

Serverless platforms in the literature (RQ5)

The software platforms are generally implemented to deal with resources from several clouds and ensure proper running of client applications. The heterogeneous nature of the cloud providers’ infrastructure (hardware and operating systems) led to the necessity to direct the developers’ focus to the functional part, rather than the underlying infrastructure [ 199 ].

With the emergence of the first serverless platform, AWS Lambda by Amazon in 2014 [ 8 ], cloud computing has evolved to a new generation referred to as serverless computing. However, serverless was not a brand-new paradigm; it emerged after the advancements in adopting virtual machines and container technologies [ 120 ]. By 2016, other competitors, namely Google, Microsoft, and IBM followed the trend. Several commercial and open-source platforms offer serverless computing. The well-known commercial systems are AWS Lambda, Google Cloud Functions, and Azure functions. Alternately, there are several open source platforms available including IBM Cloud Functions, and Apache OpenWhisk.

There are several criteria to help developers in selecting a serverless platform: cost, performance, supported programming languages and model, deployment easiness, easiness in composing functions from different providers, security, and monitoring and debugging tools [ 184 ].

Table  7 presents the serverless platforms used in the considered papers of this study. It can be noted that “AWS Lambda”, “Apache OpenWhisk”, and “Azure Functions” are the most used platforms with 78, 23, and 11 published papers, respectively. However, it is worth mentioning that each platform has its own set of features and differs from others.

The application areas of serverless computing in the literature (RQ6)

Serverless computing can be utilized in a number of application areas as follows:

A chatbot application is developed using serverless computing, which enables interaction with users via voice or text commands. Within this application, six functionalities have been implemented, namely the Date, News, Jokes, Weather, Music Tutor, and Alarm Service. For example, a user can ask for the current date using a voice or text command. The request is routed to a required serverless action on OpenWhisk for further processing. The Date action is activated via the issued command and retrieves the current date to the user in the form of text or voice [ 44 ].

Another example is the ticketing chatbot service developed using serverless computing and natural language processing (NLP). The architecture of the system consists of three parts: (1) the node.js Webhook, which works based on hypertext transfer protocol (HTTP) POST or GET requests (2) Wit.AI, which is a NLP service (3) Ticket.com, which is a ticketing order API. For example, when a user books a flight ticket; a specific function on Webhook will be activated, which routes the user query to the Wit.AI service. Wit.AI will process the query and extract useful parameters from the request such as destination, date, and time, then send it back to Webhook. After receiving the processed query from Wit.Ai; another action will be triggered and pass the processed query to Tickt.com API to retrieve flight information such as the flight number, airline name, departure time, and ticket price from several airline companies. Finally, Webhook will provide flight information to the user [ 44 , 179 , 248 ].

Information retrieval

A search engine web-based application is developed based on serverless architecture. Search engine functionalities are implemented as Amazon lambda functions. The search engine executes all the details of retrieval processing after receiving the user query (e.g., tokenization, stop-word removal, term weighting, similarity calculation, and ranking). Then, it sends back the results to the user as documents stored in the DynamoDB database to be accessed using the web application interface [ 173 ].

File processing

Serverless computing can been utilized in file processing applications [ 119 , 249 ]. For instance, in [ 119 ] a model for highly parallel file processing applications based on serverless architecture is proposed. This model provides users with different ways to process their files.

The first method is by using the API gateway. In this method, users submit files using the HTTP request employing the API gateway to a lambda function to process the file (e.g., medical images and video files).

The second method is by uploading/reading files to the Amazon simple storage service (Amazon S3) bucket. This method provides the user with three different ways to execute a lambda function using S3 buckets: (a) by uploading a file to S3 buckets. When the file is uploaded, S3 creates an event to invoke a lambda function; (b) by copying a file from another bucket to the bucket linked with the lambda function. This will cause the trigger of an event from S3 to invoke a lambda function as in the previous manner; (c) by specifying a bucket where the files to be processed are stored. Then, for each file found, the lambda function is invoked in parallel using an S3 bucket.

The third method is by specifying the output file. By this method, the user can set a chain of lambda functions to be invoked by S3 buckets. In this case, the user defines the input/output buckets for each of the lambda functions. Thus, the output bucket can be used as an input to another lambda function [ 119 ]. Here, serverless functions can handle different types of data (stored in files) such as sensory, textual, and biological data [ 200 ]. Also, many preprocessing operations using NLP may be applied to data files before processing, such as stemming and noise removal [ 78 ].

A MATLAB simulation scenario is created to illustrate the use of the smart grid with serverless cloud computing to control and manage electrical loads (devices). In this scenario, the Simulink tool is employed for simulation. A MATLAB program is developed to indicate the start and end of the simulated grid model via a batch file. The batch file is used to upload grid model data generated by the program to Amazon S3. Afterwards, a lambda function in the serverless side will be activated to process the uploaded data, and subsequently the result will be sent back to the batch file as a response. In return, the program will read continuously the response from the batch file and interpret its content to manage the electrical switch (loads) [ 201 ].

Also, An electrical overload warning system is implemented in the smart grid, based on serverless architecture. The system uses some Amazon web services, including S3, lambda functions, simple notification service (SNS), and CloudWatch. S3 is used as a storage service in the system. Lambda functions constitute a computing service that executes the code of the application. CloudWatch is a monitoring tool that monitors AWS resources and applications. The SNS is a notification service that sends and receives notifications.

The main sections of this warning system consist of data collection, data acquisition, data analysis, data mining, conclusion verification, and conclusion publishing. In this architecture, the AWS Lambda is used in data analysis and data mining. AWS CloudWatch is used for data conclusion verification. The SNS is used to generate alarms. For instance, the data is uploaded to S3, and subsequently, a lambda function is activated for data analysis and data mining. After the lambda function execution, its log data is stored in CloudWatch logs. CloudWatch is used for conclusion verification. CloudWatch defines an alarm size to a specific value, upon which it compares the value of log data with a predefined alarm size to check the current state. Then, the CloudWatch uses SNS for publishing conclusions. If the receiving data is greater than the alarm size, an alarm signal will be triggered and send an email via SNS [ 5 ].

An automated threat detection system is introduced using serverless cloud computing and Kubernetes. Kubernetes is an open source system to automatically deploy and manage application containers [ 243 , 250 ]. The system deals with threats (e.g., software vulnerabilities and insecure configurations) automatically based on user-defined policies. The system includes a vulnerability scanner (VS), which is a thread detection component. Whenever users deploy new application containers, the containers are registered with the VS, and a scanner agent is installed. When a thread is detected by the scanner, a notification is sent to the OpenWhisk component, which activates a serverless function that takes actions to reduce the threat. OpenWhisk will invoke a Kubernetes API extension and let the security enforcement operator (SEO) handle the operation [ 35 ].

Serverless cloud computing has been employed in different networking domains[ 175 , 188 , 251 , 252 ]. In [ 188 ], a variety of networking fields including software-defined networking (SDN) which can utilize advantages of serverless computing architecture have been discussed. The SDN is a network architecture approach that enables the network to be manageable and adaptive. This architecture separates the network control plane from the forwarding functions (the data plane). This decoupling enables network switches to become a simple forwarding device, and the network control is implemented as a network application that executed on a logically centralized controller. Serverless computing can be used in the SDN controllers. These controllers can be implemented as independent functions deployed on serverless platforms. For example, when a packet arrives to the SDN forwarding device, the device will parse the packet header and forward it to the SDN controller. The functions within the SDN controller will be activated then it will determine what action to be taken with the packet. After that, it will send the information to the forwarding device. The action might be modifying the header, dropping the packet, etc.

Serverless computing has been utilized in many IoT applications, as shown in Table  8 . For example, a camera can be installed to monitor a house, after which processing images captured by the camera can be performed by some serverless functions provided by the OpenWhisk platform. When a camera detects an interesting object such as a car or a human, the camera sends its pictures to the serverless platform for further processing. To extract features, a serverless function is called to perform feature extraction and then reports its status to the users [ 232 ].

Edge computing

Serverless cloud computing and edge computing have been used to build different kinds of applications, as presented in Table  8 . For instance, the authors of [ 217 ] have implemented an autonomous mobile robot (AMR) system based on serverless computing and edge computing. The system consists of three main components: an AMR with NVIDIA Jetson TX2 module for edge computing, a serverless architecture based on AWS, and a cross-platform mobile application developed using React Native. The main idea of the system is to deliver a package to a user. For example, the user will interact with the mobile application to send a package. Once the delivery request has been received from the user, the AWS IoT can activate related lambda functions, such as position coordinate. Then, the AMR would start its mission, sending the package to the receiver’s location. Also, facial images were regularly retrieved by AWS lambda to identify the receiver’s face. Finally, the task is completed when the receiver’s identification is confirmed [ 217 ].

Serverless computing challenges and issues (RQ7)

Studying the literature reveals a number of challenges and issues posed by employing serverless computing. These challenges cover the functional and non-functional aspects of serverless computing as follows:

Cost and pricing model

Cost is a fundamental challenge; therefore, serverless computing providers should reduce the usage of resources to the minimum, while functioning in both execution and idle states. Further, the pricing model is another challenge in serverless computing compared to other cloud computing approaches. For example, the CPU bound is cheap, whereas the input/output (I/O) bound functions may be more expensive from dedicated servers. Table  9 presents papers that investigate issues on cost and pricing models in serverless cloud computing.

Serverless computing can scale to zero while there is no request for functions and services. Scaling to zero leads to a problem called cold start. A cold start occurs when serverless functions remain idle for some time, and the next time these functions are invoked, a longer start time is required. Methods and techniques to reduce the cold start problem are crucial as a result, many papers have been studied this problem, as shown in Table  9 .

Resource limits

In serverless computing, resources are required to ensure that the platform can deal with load increasing. This includes CPU usage, memory, execution time, and bandwidth [ 94 , 202 , 210 , 235 , 280 ].

Security is the most challenging issue in serverless cloud computing. One of the security issues is isolation, because functions are running on a shared platform by many users. Therefore, strong isolation is required. Another security issue is trust when it comes to process-sensitive data. The serverless applications work with many system components, which must function correctly to maintain security properties. Table  9 presents several papers associated with serverless security.

Serverless computing must ensure function scalability and elasticity. For example, when many requests are issued to a serverless application, these requests should all be served and the used serverless cloud provider should provide the required resources to process all these requests and should scale up with the number of requests [ 210 , 280 , 281 ].

Long-running

Serverless computing runs function in a limited and short execution time, while there are some tasks might require long execution time. This does not support long execution running, since these functions are stateless, which means that if the function is paused it cannot be resumed again [ 11 , 202 , 234 , 280 ].

Programming & debugging

There is currently a lack of debugging tools. Further, monitoring tools are required, since developers need to monitor the application and observe how functions are working. More advanced integrated development environments (IDEs) are needed, so developers can perform refactoring functions, such as merging or splitting functions, and reverting functions to the previous version. Moreover, logs from serverless function invocations need to be sent to the developer and provide detailed stack traces. When an error occurs, a good method is required to report details on the occurrence to the developer. The equivalent of a stack trace for serverless computing is currently not available. Table  9 shows many papers that consider programming and debugging challenges and issues.

Vendor lock-in

The FaaS paradigm separates the code from the data, which leads the functions to depend strongly on the could provider’s ecosystem for storing, obtaining, and transferring data [ 282 ]. This issue makes the customers dependent on the serverless provider for products and services, and the customers cannot easily use different vendors in the future without substantial cost. Thus, customers have to wait on the serverless provider for additional services [ 9 , 130 , 202 ].

Performance

Serverless computing has many performance challenges and issues such as scheduling and service calling overhead. For instance, scheduling means when a serverless function is activated in response to an event this function should be mapped to a specific resource (e.g., container or VM) to be run. The resource can have a significant effect on performance based on available resources, location of input data and code, load balancing, etc. Table  9 shows papers related to serverless performance.

Fault tolerance

It refers to a system that continues working and provides its services despite the failure in some components. It mostly occurs when some containers fail. To overcome this challenge, a basic retry mechanism is used [ 11 , 210 , 235 ].

Function composition

Serverless cloud vendors provide users the ability to deploy small stateless functions to the cloud to handle a specific task. However, some complex tasks require multiple functions to work with each other collaboratively to be performed. Therefore, more research needs to be done on how function composition can be used effectively and efficiently in serverless cloud computing [ 11 , 38 , 235 ].

Resource sharing

Functions in serverless cloud computing share resources to achieve inexpensive cloud computing. Sharing resources among functions and other serverless components is a challenging task. Therefore, good techniques are required to be investigated to achieve this goal [ 98 , 210 , 283 ].

A serverless application consists of many small functions. These functions work together to accomplish the application’s functionality. Therefore, integration testing for these functions is a crucial issue to make sure that the application works properly [ 9 , 84 , 284 ].

Naming and addressing system

Users deploy functions to serverless cloud computing to solve problems. These functions cannot listen to network communications. The existing serverless cloud computing frameworks do not support this feature. Instead, they use third party services such as Amazon S3 to communicate with other functions. Therefore, finding the internet protocol (IP) address of a function by other functions and services is a challenging issue in serverless cloud computing [ 98 ].

Legacy systems

Legacy systems refer to old technologies, techniques, hardware, and software systems that are still in use. It should be possible to reach these systems from serverless cloud computing. Also, these systems might be required to be transferred to cloud computing. Therefore, more work needs to be done on the migration process and how the functions can be extracted from legacy systems to be deployed as serverless cloud functions [ 84 , 119 , 120 , 210 , 280 ].

Managing hybrid cloud

In a hybrid cloud, a developer may deploy an application to different clouds (hybrid cloud). For example, if some functions of an application are on a specific serverless cloud vendor and others are hosted on other public clouds; then, managing these functions and their interactions is a challenging issue [ 84 , 210 , 280 ].

Lack of quality of service (QoS) support

Existing serverless platforms and frameworks do not provide users the control over the QoS of serverless functions [ 235 ]. Cold starts, queuing, and orchestration are the main reasons affecting the QoS in serverless computing [ 8 ].

Architecture complexity

A serverless application may consist of several functions; the number of functions increases the complexity of the architecture. Managing these functions and services related to the application also leads to a complex architecture [ 9 ].

Interactions tracking

Stateful requests are usually used by real-life applications. It means deployed systems keep track of the state of users’ interactions and store them on the server-side for further uses. However, in stateless serverless functions, it is not obvious how these functions will handle and manage the states of each user [ 210 , 280 ].

Concurrency management

Concurrency means a function can handle any number of requests whenever a function is invoked. For example, if a request has been made to a serverless function, the function will process that request. However, if another request has been made to that function and the function is still processing the previous request, then the serverless should provide another instance of that function to serve the new request [ 210 , 280 ].

Support for heterogeneous hardware

Existing serverless platforms may not support some specialized hardware such as graphics processing unit (GPU) and field programmable gate arrays (FPGAs). This is a challenging issue for vendors to provide support for heterogeneous hardware [ 210 , 280 ].

Tools available for serverless computing (RQ8)

Nowadays, various providers strive to facilitate the adjustable use and allocation of machine resources on the cloud [ 9 ]. Likewise, plenty of supportive tools and services are aiding developers to more efficiently manage and deploy applications using serverless computing. Serverless computing is auto-scalable, reliable, and easily accessible [ 203 ]; for these reasons, big cloud providers such as Amazon, Microsoft, Google, IBM have realized the importance of offering frameworks, IDEs, software development kits (SDKs), function development kits (FDK), migrating mechanisms, logs, and monitoring tools to enhance and simplify the development, testing, deployment, and monitoring of serverless applications [ 17 ]. For instance, Amazon offers Cloud9 IDE for local deploying and testing [ 205 ].

Apart from the cloud providers’ specific tools, plenty of third-party tools exist for the developers. With the concept of these tools, developers can build and deploy applications on multi-cloud providers. Developers are also able to control platforms and resources by programming. The advantages of this are linking the applications with auto-scaling controllers and including advanced self-mechanisms into the code to automatically configure, secure, optimize, and recover the cloud applications. The core advantage of this feature is the acceleration in applying changes to the application environment [ 272 ].

There are several tools available to model serverless applications, which are based on deployment models as either imperative or declarative. The imperative model defines the execution steps to obtain a specific deployment task. While the declarative model describes the structure of a desired application deployment. However, to fully benefit from employing a serverless architecture, cloud providers should address issues that have arisen with the use of a serverless paradigm. For instance, debugging tools are unable to track and identify the exact reason behind errors [ 44 ], as most of them are limited to what cloud providers offer [ 179 ]. Although many powerful tools have been mentioned in this study and can be used in serverless computing in real scenarios, there is still a great opportunity to develop further tools and services.

Migration of monolithic applications to serverless computing (RQ9)

The nature of most existing applications is monolithic. Monolithic applications have several drawbacks; they are characterized by continuous growth in complexity and size over time.

The bigger size of the monolithic applications leads to slower startup time. Moreover, novice developers face difficulties in digesting the traditional programming paradigm. Economically, monolithic systems take more effort to be developed and debugged. Furthermore, integrating the latest technological development into monolithic systems is a tough and expensive process. Generally, monolithic applications are designed to be tightly coupled – the entire application will be unable to run or compile if one component is missing or fails [ 128 ]. It is also difficult to scale the application when multiple components have limited resources.

Another drawback is that updating any component will require redeployment of the entire project. The migration process to serverless computing involves transferring the legacy application code to serverless functions. This process could be more efficient and functional in applications with less size [ 76 ].

The key challenging aspect of migration is about extracting the serverless computing from the monolithic systems. There are several approaches to accomplish this task, one of them is Lift and shift [ 205 ]. This technique transfers the whole infrastructure to the cloud, however, this method also brings the already existing problems within the source to the destination. In [ 205 ] the authors proposed toLambda to automatically refactor, test, and deploy the monolithic applications (Java) into microservices (AWS Lambda Node.js). While rebuilding the legacy application from scratch is recommended for applications that no longer depend on the existing cloud services [ 130 ].

However, not all applications are suitable for migration to serverless computing [ 76 , 128 ]; therefore, the first important aspect to be considered before rebuilding the applications is whether it would save money [ 188 ]. For such cases, newly desired features could be implemented and added via serverless computing as an extension to the current systems [ 128 ].

The other approach is to refactor the entire legacy code into FaaS services. During the migration phase, it is crucial to address the coupling of the systems not only in the application logic but also in the databases, as more functions will call the same database. However, migrating the server-side while keeping the user interface could lead to problems. Moreover, the client cannot obtain integrated data by a single request. As the functions are decoupled into smaller entities, the server is unable to aggregate data from different entities. Thus, it is the client’s responsibility to call the necessary entities to achieve this task [ 76 ].

Future directions of research (RQ10)

As the evolution of serverless computing is relatively new, there are several research paths available to be focused on as follows:

Function startup

One of the major research opportunities is overcoming the cold start problem without affecting the primary feature of serverless which is scaling to zero [ 160 , 188 ]. The first call of functions needs initializing the required libraries, which will cause a cold start. To bypass this, the computing resources will be warm for a certain time. Hence, upcoming requests will be handled faster. This could be performed via enhancing scheduling policies and developing more accurate function performance measurements [ 86 ]. Serverless providers follow their approaches to keep the functions in the warm pool. However, most of them are based on the number of requests for a certain time. Thus, if a function is not called frequently, it will suffer again from the cold start.

Very few studies such as [ 272 ] suggested a periodic event scheduler for Kotless (a serverless framework for Kotlin) which will trigger a list of warm functions every few minutes. The authors of the study claimed that this will reduce the cold start without bringing extra costs. While in [ 233 ] argues that pre-warming methods are unnecessarily using resources with idle containers. The researchers are still working to avoid cold start by reducing high delayed function startups via optimizing compute resources [ 11 ].

Recycling and rebalancing minutes and hours of idle runtime is an expensive process for cloud providers. Therefore, reducing the cold start penalties will help cloud providers in the first place and hence customers. The authors in [ 202 ] proposed FaaStest an autonomous approach based on machine learning to capture the function call behavior and then dynamically select the optimal ones. This technique could reduce the cold start by 90%. They proposed a strategy to predict functions invoking time and warming the function using fine-grained regression method [ 285 ]. However, overcoming the issue of function startups is still considered as a research direction to be more investigated.

Keeping a guaranteed QoS level in the software level agreement (SLA) that describes the lower service level offered by the service providers [ 166 ] is a major obstacle for cloud providers to offer optimal performance metrics [ 167 , 207 ]. However, serverless frameworks should consider the objectives of both providers and users [ 242 ]; customers and developers have none or little QoS support over the functions [ 236 ]. In addition, the auto-scaling feature lacks QoS guarantees. This lack of QoS affects the performance of serverless applications. Increasing response time leads to decreasing the QoS level [ 207 ]. It also raises the cost of the service [ 236 ]. Therefore, achieving an ideal resource allocation management is a complicated and challenging task as several objectives should be fulfilled together [ 209 ]. Hence, providing more efficient QoS management of functions by the auto-scaling is essential to be considered without degrading the fault-tolerance features and increasing the cost.

Pricing is crucial for both customers and cloud providers. However, there is a shortage in pricing models, as there is an imbalance in needs between serverless providers, developers, and service end-users [ 236 ]. The pricing scheme for most cloud providers is based on the number of functions’ requests and execution time-the quantity of consumed resources [ 123 , 200 ]. Currently, FaaS is less expensive when functions are bound to I/O than CPU. Moreover, services that dynamically adjust resource consumption are unable to predict the optical computing technology. It is crucial to implement solutions that offer cost-effective computing resources. FaStest reduced the cost by 50% via learning the behavioral pattern of functions using machine learning [ 202 ]. Price estimation has a great impact on selecting the most optimal provider. Therefore there should be more researches on developing tools to predict the pricing in advance.

Since the serverless emergence, researchers are working on the open question of how to decompose legacy systems into FaaS without degrading performance [ 208 ]. Several works have been done on migrating to FaaS [ 76 , 130 , 286 ]. The currently available automated tools for migrating legacy code into FaaS are not fully practical due to the remaining manual work that needs to be done [ 17 ]. Therefore, finding optimal automatic migration solutions for existing legacy systems is an interesting research direction [ 130 ]. Moreover, research on tools for checking whether a legacy system will fit the serverless paradigm is a crucial line. Also, developing and enhancing automatic and semi-automatic analysis strategies based on artificial intelligence could be another future research field.

Debugging, testing, and benchmarking

The available tools for testing, debugging, and deployment are immature, this prevents some developers from entering the serverless environment. The shortage of tools in FaaS is a core problem, particularly the testing tools [ 17 ]. Moreover, most FaaS environments lack powerful local emulation platforms for testing. Therefore, developers are mostly depending on the server-side, which is expensive. Developers need to be ensured about the adequate testing tools before diving into the serverless world. A challenging aspect in benchmarking is the lack of information due to the heterogeneity of the cloud provider data center: hardware, software, and configurations [ 287 ]. Additionally, benchmarking FaaS platforms should take advantage of analyzing the cloud services, which lacks limited accessible measurements and hidden modification of services over time [ 55 ]. Thus, it is essential to have transparent, fair, and standardized benchmarking tools available for developers.

Threats to validity

Several threats might impact the validity of the literature mapping studies. In this paper, popular instructions and guidelines were taken into account to avoid threats to validity as follows:

Coverage of research questions: All up-to-date research aspects of serverless computing might not be included in this study. To overcome this threat, the brainstorming was conducted by all the authors in determining the most current research questions in the area.

Coverage of related papers: The process of obtaining all the related studies in serverless computing cannot be secured. In this study, various literature databases were employed; moreover, the method based on different terms and synonymous is followed by all the authors in determining the related questions.

Paper inclusion/exclusion criteria: The individual bias and interpretation could affect the implementation of the criteria. Therefore, to solve this problem, the agreements of all authors were considered in excluding or including a paper.

Accuracy of data extraction: The individual experience effects extracting the data, therefore online meetings were conducted after the data extraction process by each author. During the meetings, the outcomes from each author were compared with other findings to determine the differences and reach a final consensus.

Reproducibility of the study: Whether other researchers could obtain similar outcomes of this study is another threat. Thus, to address this, the research methodology contains the well-explained steps and actions conducted in this paper (as shown in “ Research methodology ” section).

Conclusions

The contributions of the work presented in this paper are threefold: (a) a methodical review of related literature on the topic of serverless computing, to address the issue of the lack of compiling information on the state-of-the-art of the field; (b) a comparison of the platforms and tools used in serverless computing; (c) an extensive analysis of the differences, benefits, and issues related to serverless computing, to provide a more complete understanding of the topic. Given the fast evolution and growing interest in the field, this survey focused on gathering the most outstanding trends and outcomes of serverless computing, as described by recent researchers. This survey could significantly reduce ambiguity and the entry barrier for novice developers to adapt to the serverless environment. Furthermore, the findings presented in this study could be of great value for future researchers interested in further investigating serverless computing. Finally, it is worth mentioning that the interest that both commercial and academic efforts fueled into studying, developing, and implementing serverless tools in forthcoming years could help maximize the potential that serverless computing could bring to the IT community.

Availability of data and materials

Not applicable.

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Software Engineering and Embedded Systems (SEES) Research Group, College of Medicine, University of Duhok, Duhok, Kurdistan Region, Iraq

Hassan B. Hassan

Software Engineering and Embedded Systems (SEES) Research Group, Department of Computer Science, College of Science, University of Duhok, Duhok, Kurdistan Region, Iraq

Saman A. Barakat & Qusay I. Sarhan

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Authors’ contributions.

Conceptualization: HBH, SAB, and QIS; methodology: HBH, SAB, and QIS; validation: HBH, SAB, and QIS; formal analysis: HBH, SAB, and QIS; investigation: HBH, SAB, and QIS; resources: HBH; data curation, HBH and SAB; writing—original draft preparation: HBH, SAB, and QIS; writing—review and editing: HBH, SAB, and QIS; visualization: SAB; supervision: QIS; It is noted that all authors cooperated with each other to achieve suitable information flow across the entire paper. The authors read and approved the final manuscript.

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Hassan B. Hassan received the B.Sc. degree in Computer Science from University of Duhok, Iraq, in 2010. He completed the M.Sc. degree in Web Applications and Services, from Leicester University, UK, in 2015. He is currently working as an assistant lecturer at the college of medicine, University of Duhok, Iraq. His main areas of research interest are cloud computing, web programming, big data, and human computer interaction.

Saman A. Barakat received the B.Sc. degree in Computer Science from University of Duhok, Iraq, in 2008. He completed the M.Sc. degree in Advanced Computer Science, from Newcastle University, UK, in 2012. He is currently working as a lecturer at the college of science, University of Duhok, Iraq. His main areas of research interest are cloud computing, and software engineering.

Qusay I. Sarhan received the B.Sc. degree in Software Engineering from University of Mosul, Iraq, in 2007 and the M.Tech. degree in Software Engineering from Jawaharlal Nehru Technological University, India, in 2011. Currently, he is a lecturer and the leader of Software Engineering and Embedded Systems (SEES) research group at University of Duhok, Iraq. He has a couple of national and international publications and his research interests include software engineering, internet of things, and embedded systems.

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Hassan, H.B., Barakat, S.A. & Sarhan, Q.I. Survey on serverless computing. J Cloud Comp 10 , 39 (2021). https://doi.org/10.1186/s13677-021-00253-7

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DOI : https://doi.org/10.1186/s13677-021-00253-7

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Green cloud computing adoption challenges and practices: a client’s perspective-based empirical investigation

• Original Article
• Published: 11 August 2023
• Volume 25 , pages 427–446, ( 2023 )

Cite this article

• Ashfaq Ahmad 1 ,
• Rafiq Ahmad Khan 1 , 2 ,
• Siffat Ullah Khan 1 ,
• Hathal Salamah Alwageed 3 ,
• Abdullah A. Al-Atawi 4 &
• Youngmoon Lee 5  

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Over the last decade, the widespread adoption of cloud computing has spawned a new branch of the computing industry known as green cloud computing. Cloud computing is improving, and data centers are increasing at regular frequencies to meet the demands of users. On the other hand, cloud providers pose major environmental risks because massive data centers use a large amount of energy and leave a carbon footprint. One possible solution to this issue is the use of green cloud computing. However, clients face significant difficulties in adopting green cloud computing. This study aims to understand the problems faced by client organizations while considering green cloud computing. In addition, this study aims to empirically identify the solution to the challenges faced by green cloud computing practitioners. A questionnaire survey approach was used to get insight into green cloud computing practitioners concerning the challenges they faced and their solutions. Data were obtained from sixty-nine professionals in green cloud computing. The results revealed that “lack of quality of service”, “lack of dynamic response”, and “lack of services to satisfy client’s requirements” are critical for green cloud computing. In addition, sixty-three practices for addressing the challenges in green cloud computing are also identified. The identified challenges and practices of green cloud computing will benefit the client organizations to update and revise their process to consider green cloud computing. In addition, it will also assist vendor organizations in developing, planning, and managing systems concerning client satisfaction.

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Acknowledgements

This work was supported in part by the National Research Foundation of Korea (NRF) grant 2022R1G1A1003531, 2022R1A4A3018824, RS-2023-00230593 and Institute of Information and Communications Technology Planning and Evaluation (IITP) grant ITP-2023-2020-0-01741, RS-2022-00155885 funded by the Korea government (MSIT).

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Software-Engineering-Research-Group (SERG-UOM), Department of Computer Science and IT, University of Malakand, Chakdara, Pakistan

Ashfaq Ahmad, Rafiq Ahmad Khan & Siffat Ullah Khan

School of Software, Northwestern Polytechnical University, Chang’an, Xian, 710129, Shaanxi, People’s Republic of China

Rafiq Ahmad Khan

College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia

Hathal Salamah Alwageed

Department of Computer Science, Applied College, University of Tabuk, 47512, Tabuk, Saudi Arabia

Abdullah A. Al-Atawi

Department of Robotics, Hanyang University, Ansan, 15588, Korea

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AA conducted SLR and the RAK and SUK reviewed all its steps. HSA and AAA randomly review all the extracted data from the SLR process. In the second step, AA conducted an empirical study. The RAK and YL conduct the pilot study. AAA and YL synthesis and validate the data accordingly.

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Correspondence to Rafiq Ahmad Khan or Youngmoon Lee .

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Appendix 1: Empirical investigation of green cloud computing challenges faced by client organization

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Ahmad, A., Khan, R.A., Khan, S.U. et al. Green cloud computing adoption challenges and practices: a client’s perspective-based empirical investigation. Cogn Tech Work 25 , 427–446 (2023). https://doi.org/10.1007/s10111-023-00734-6

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Received : 08 April 2023

Accepted : 20 July 2023

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Issue Date : November 2023

DOI : https://doi.org/10.1007/s10111-023-00734-6

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Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale

The honeymoon phase of generative AI (gen AI) is over. As most organizations are learning, it is relatively easy to build gee-whiz gen AI pilots, but turning them into at-scale capabilities is another story. The difficulty in making that leap goes a long way to explaining why just 11 percent of companies have adopted gen AI at scale, according to our latest tech trends research. 1 “McKinsey Technology Trends Outlook 2024,” forthcoming on McKinsey.com.

About the authors

This article is a collaborative effort by Aamer Baig , Douglas Merrill , and Megha Sinha, with Danesha Mead and Stephen Xu, representing views from McKinsey Technology and QuantumBlack, AI by McKinsey.

This maturing phase is a welcome development because it gives CIOs an opportunity to turn gen AI’s promise into business value. Yet while most CIOs know that pilots don’t reflect real-world scenarios—that’s not really the point of a pilot, after all—they often underestimate the amount of work that needs to be done to get gen AI production ready. Ultimately, getting the full value from gen AI requires companies to rewire how they work , and putting in place a scalable technology foundation is a key part of that process.

Three approaches to using gen AI

There are three primary approaches to take in using gen AI:

• In “Taker” use cases, companies use off-the-shelf, gen AI–powered software from third-party vendors such as GitHub Copilot or Salesforce Einstein to achieve the goals of the use case.
• In “Shaper” use cases, companies integrate bespoke gen AI capabilities by engineering prompts, data sets, and connections to internal systems to achieve the goals of the use case.
• In “Maker” use cases, companies create their own LLMs by building large data sets to pre-train models from scratch. Examples include OpenAI, Anthropic, Cohere, and Mistral AI.

Most companies will turn to some combination of Taker, to quickly access a commodity service, and Shaper, to build a proprietary capability on top of foundation models. The highest-value gen AI initiatives, however, generally rely on the Shaper approach. 1 For more on the three approaches, see “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023.

We explored many of the key initial technology issues in a previous article . 2 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. In this article, we want to explore seven truths about scaling gen AI for the “Shaper” approach, in which companies develop a competitive advantage by connecting large language models (LLMs) to internal applications and data sources (see sidebar “Three approaches to using gen AI” for more). Here are seven things that Shapers need to know and do:

• Eliminate the noise, and focus on the signal. Be honest about what pilots have worked. Cut down on experiments. Direct your efforts toward solving important business problems.
• It’s about how the pieces fit together, not the pieces themselves. Too much time is spent assessing individual components of a gen AI engine. Much more consequential is figuring out how they work together securely.
• Get a handle on costs before they sink you. Models account for only about 15 percent of the overall cost of gen AI applications. Understand where the costs lurk, and apply the right tools and capabilities to rein them in.
• Tame the proliferation of tools and tech. The proliferation of infrastructures, LLMs, and tools has made scaled rollouts unfeasible. Narrow down to those capabilities that best serve the business, and take advantage of available cloud services (while preserving your flexibility).
• Create teams that can build value, not just models. Getting to scale requires a team with a broad cross-section of skills to not only build models but also make sure they generate the value they’re supposed to, safely and securely.
• Go for the right data, not the perfect data. Targeting which data matters most and investing in its management over time has a big impact on how quickly you can scale.
• Reuse it or lose it. Reusable code can increase the development speed of generative AI use cases by 30 to 50 percent.

1. Eliminate the noise, and focus on the signal

Although many business leaders acknowledge the need to move past pilots and experiments, that isn’t always reflected in what’s happening on the ground. Even as gen AI adoption increases, examples of its real bottom-line impact are few and far between. Only 15 percent of companies in our latest AI survey say they are seeing use of gen AI have meaningful impact on their companies’ EBIT. 3 That is, they attribute 5 percent or more of their organizations’ EBIT to gen AI use. McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com.

Exacerbating this issue is that leaders are drawing misleading lessons from their experiments. They try to take what is essentially a chat interface pilot and shift it to an application—the classic “tech looking for a solution” trap. Or a pilot might have been deemed “successful,” but it was not applied to an important part of the business.

There are many reasons for failing to scale, but the overarching one is that resources and executive focus are spread too thinly across dozens of ongoing gen AI initiatives. This is not a new development. We’ve seen a similar pattern when other technologies emerged, from cloud to advanced analytics. The lessons from those innovations, however, have not stuck.

The most important decision a CIO will need to make is to eliminate nonperforming pilots and scale up those that are both technically feasible and promise to address areas of the business that matter while minimizing risk (Exhibit 1). The CIO will need to work closely with business unit leaders on setting priorities and handling the technical implications of their choices.

2. It’s about how the pieces fit together, not the pieces themselves

In many discussions, we hear technology leaders belaboring decisions around the component parts required to deliver gen AI solutions—LLMs, APIs, and so on. What we are learning, however, is that solving for these individual pieces is relatively easy and integrating them is anything but. This creates a massive roadblock to scaling gen AI.

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

The challenge lies in orchestrating the range of interactions and integrations at scale. Each use case often needs to access multiple models, vector databases, prompt libraries, and applications (Exhibit 2). Companies have to manage a variety of sources (such as applications or databases in the cloud, on-premises, with a vendor, or a combination), the degree of fidelity (including latency and resilience), and existing protocols (for example, access rights). As a new component is added to deliver a solution, it creates a ripple effect on all the other components in the system, adding exponential complexity to the overall solution.

Main components for gen AI model orchestration

Orchestration is the process of coordinating various data, transformation, and AI components to manage complex AI workflows. The API (or LLM) gateway layer serves as a secure and efficient interface between users or applications and underlying gen AI models. The orchestration engine itself is made up of the following components:

• Prompt engineering and prompt library: Prompt engineering is the process of crafting input prompts or queries that guide the behavior and output of AI models. A prompt library is a collection of predefined prompts that users can leverage as best practices/shortcuts when they invoke a gen AI model.
• Context management and caching: Context management highlights background information relevant to a specific task or interaction. Caching relates to storing previously computed results or intermediate data to accelerate future computations.
• Information retrieval (semantic search and hybrid search): Information-retrieval logic allows gen AI models to search for and retrieve relevant information from a collection of documents or data sources.
• Evaluation and guardrails: Evaluation and guardrail tools help assess the performance, reliability, and ethical considerations of AI models. They also provide input to governance and LLMOps. This encompasses tools and processes for evaluating model accuracy, robustness, fairness, and safety.

The key to effective orchestration is embedding the organization’s domain and workflow expertise into the management of the step-by-step flow and sequencing of the model, data, and system interactions of an application running on a cloud foundation . The core component of an effective orchestration engine is an API gateway, which authenticates users, ensures compliance, logs request-and-response pairs (for example, to help bill teams for their usage), and routes requests to the best models, including those offered by third parties. The gateway also enables cost tracking and provides risk and compliance teams a way to monitor usage in a scalable way. This gateway capability is crucial for scale because it allows teams to operate independently while ensuring that they follow best practices (see sidebar “Main components for gen AI model orchestration”).

The orchestration of the many interactions required to deliver gen AI capabilities, however, is impossible without effective end-to-end automation. “End-to-end” is the key phrase here. Companies will often automate elements of the workflow, but the value comes only by automating the entire solution, from data wrangling (cleaning and integration) and data pipeline construction to model monitoring and risk review through “policy as code.” Our latest research has shown that gen AI high performers are more than three times as likely as their peers to have testing and validation embedded in the release process for each model. 4 We define gen AI high performers as those who attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com. A modern MLOps platform is critical in helping to manage this automated flow and, according to McKinsey analysis, can accelerate production by ten times as well as enable more efficient use of cloud resources.

Gen AI models can produce inconsistent results, due to their probabilistic nature or the frequent changes to underlying models. Model versions can be updated as often as every week, which means companies can’t afford to set up their orchestration capability and let it run in the background. They need to develop hyperattentive observing and triaging capabilities to implement gen AI with speed and safety . Observability tools monitor the gen AI application’s interactions with users in real time, tracking metrics such as response time, accuracy, and user satisfaction scores. If an application begins to generate inaccurate or inappropriate responses, the tool alerts the development team to investigate and make any necessary adjustments to the model parameters, prompt templates, or orchestration flow.

3. Get a handle on costs before they sink you

The sheer scale of gen AI data usage and model interactions means costs can quickly spiral out of control. Managing these costs will have a huge impact on whether CIOs can manage gen AI programs at scale. But understanding what drives costs is crucial to gen AI programs. The models themselves, for example, account for only about 15 percent of a typical project effort . 5 “ Generative AI in the pharmaceutical industry: Moving from hype to reality ,” McKinsey, January 9, 2024. LLM costs have dropped significantly over time and continue to decline.

CIOs should focus their energies on four realities:

Change management is the biggest cost. Our experience has shown that a good rule of thumb for managing gen AI costs is that for every $1 spent on developing a model, you need to spend about $3 for change management. (By way of comparison, for digital solutions, the ratio has tended to be closer to $1 for development to $1 for change management . 6 Eric Lamarre, Kate Smaje, and Rodney Zemmel, “ Rewired to outcompete ,” McKinsey, June 20, 2023. ) Discipline in managing the range of change actions, from training your people to role modeling to active performance tracking, is crucial for gen AI. Our analysis has shown that high performers are nearly three times more likely than others to have a strong performance-management infrastructure, such as key performance indicators (KPIs), to measure and track value of gen AI. They are also twice as likely to have trained nontechnical people well enough to understand the potential value and risks associated with using gen AI at work. 7 McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com.

Companies have been particularly successful in handling the costs of change management by focusing on two areas: first, involving end users in solution development from day one (too often, companies default to simply creating a chat interface for a gen AI application), and second, involving their best employees in training models to ensure the models learn correctly and quickly.

• Run costs are greater than build costs for gen AI applications. Our analysis shows that it’s much more expensive to run models than to build them. Foundation model usage and labor are the biggest drivers of that cost. Most of the labor costs are for model and data pipeline maintenance. In Europe, we are finding that significant costs are also incurred by risk and compliance management.
• Driving down model costs is an ongoing process. Decisions related to how to engineer the architecture for gen AI, for example, can lead to cost variances of 10 to 20 times, and sometimes more than that. An array of cost-reduction tools and capabilities are available, such as preloading embeddings. This is not a one-off exercise. The process of cost optimization takes time and requires multiple tools, but done well, it can reduce costs from a dollar a query to less than a penny (Exhibit 3).
• Investments should be tied to ROI. Not all gen AI interactions need to be treated the same, and they therefore shouldn’t all cost the same. A gen AI tool that responds to live questions from customers, for example, is critical to customer experience and requires low-latency rates, which are more expensive. But code documentation tools don’t have to be so responsive, so they can be run more cheaply. Cloud plays a crucial rule in driving ROI because its prime source of value lies in supporting business growth, especially supporting scaled analytics solutions. The goal here is to develop a modeling discipline that instills an ROI focus on every gen AI use case without getting lost in endless rounds of analysis.

A generative AI reset: Rewiring to turn potential into value in 2024

4. tame the proliferation of tools and tech.

Many teams are still pushing their own use cases and have often set up their own environments, resulting in companies having to support multiple infrastructures, LLMs, tools, and approaches to scaling. In a recent McKinsey survey, in fact, respondents cited “too many platforms” as the top technology obstacle to implementing gen AI at scale. 8 McKinsey survey on generative AI in operations, November 2023. The more infrastructures and tools, the higher the complexity and cost of operations, which in turn makes scaled rollouts unfeasible. This state of affairs is similar to the early days of cloud and software as a service (SaaS), when accessing the tech was so easy—often requiring no more than a credit card—that a “wild west” of proliferating tools created confusion and risk.

To get to scale, companies need a manageable set of tools and infrastructures. Fair enough—but how do you know which providers, hosts, tools, and models to choose? The key is to not waste time on endless rounds of analysis on decisions that don’t matter much (for example, the choice of LLMs is less critical as they increasingly become a commodity) or where there isn’t much of a choice in the first place—for example, if you have a primary cloud service provider (CSP) that has most of your data and your talent knows how to work with the CSP, you should probably choose that CSP’s gen AI offering. Major CSPs, in fact, are rolling out new gen AI services that can help companies improve the economics of some use cases and open access to new ones. How well companies take advantage of these services depends on many variables, including their own cloud maturity and the strength of their cloud foundations.

What does require detailed thinking is how to build your infrastructure and applications in a way that gives you the flexibility to switch providers or models relatively easily. Consider adopting standards widely used by providers (such as KFServing, a serverless solution for deploying gen AI models), Terraform for infrastructure as code, and open-source LLMs.

It’s worth emphasizing that overengineering for flexibility eventually leads to diminishing returns. A plethora of solutions becomes expensive to maintain, making it difficult to take full advantage of the services providers offer.

5. Create teams that can build value, not just models

One of the biggest issues companies are facing is that they’re still treating gen AI as a technology program rather than as a broad business priority. Past technology efforts demonstrate, however, that creating value is never a matter of “just tech.” For gen AI to have real impact, companies have to build teams that can take it beyond the IT function and embed it into the business. Past lessons are applicable here, too. Agile practices sped up technical development, for example. But greater impact came only when other parts of the organization—such as risk and business experts—were integrated into the teams along with product management and leadership.

There are multiple archetypes for ensuring this broader organizational integration. Some companies have built a center of excellence to act as a clearinghouse to prioritize use cases, allocate resources, and monitor performance. Other companies split strategic and tactical duties among teams. Which archetype makes sense for any given business will depend on its available talent and local realities. But what’s crucial is that this centralized function enables close collaboration between technology, business, and risk leads, and is disciplined in following proven protocols for driving successful programs. Those might include, for example, quarterly business reviews to track initiatives against specific objectives and key results (OKRs), and interventions to resolve issues, reallocate resources, or shut down poor-performing initiatives.

A critical role for this governing structure is to ensure that effective risk protocols are implemented and followed. Build teams, for example, need to map the potential risks associated with each use case; technical and “human-in-the-loop” protocols need to be implemented throughout the use-case life cycle. This oversight body also needs a mandate to manage gen AI risk by assessing exposures and implementing mitigating strategies.

One issue to guard against is simply managing the flow of tactical use cases, especially where the volume is large. This central organization needs a mandate to cluster related use cases to ensure large-scale impact and drive large ideas. This team needs to act as the guardians for value, not just managers of work.

One financial services company put in place clearly defined governance protocols for senior management. A steering group, sponsored by the CIO and chief strategy officer, focused on enterprise governance, strategy, and communication, driving use-case identification and approvals. An enablement group, sponsored by the CTO, focused on decisions around data architecture, data science, data engineering, and building core enabling capabilities. The CTO also mandated that at least one experienced architect join a use-case team early in their process to ensure the team used the established standards and tool sets. This oversight and governance clarity was crucial in helping the business go from managing just five to more than 50 use cases in its pipeline.

6. Go for the right data, not the perfect data

About quantumblack, ai by mckinsey.

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Misconceptions that gen AI can simply sweep up the necessary data and make sense of it are still widely held. But high-performing gen AI solutions are simply not possible without clean and accurate data, which requires real work and focus. The companies that invest in the data foundations to generate good data aim their efforts carefully.

Take the process of labeling, which often oscillates between seeking perfection for all data and complete neglect. We have found that investing in targeted labeling—particularly for the data used for retrieval-augmented generation (RAG)—can have a significant impact on the quality of answers to gen AI queries. Similarly, it’s critical to invest the time to grade the importance of content sources (“authority weighting”), which helps the model understand the relative value of different sources. Getting this right requires significant human oversight from people with relevant expertise.

Because gen AI models are so unstable, companies need to maintain their platforms as new data is added, which happens often and can affect how models perform. This is made vastly more difficult at most companies because related data lives in so many different places. Companies that have invested in creating data products are ahead of the game because they have a well-organized data source to use in training models over time.

At a materials science product company, for example, various teams accessed product information, but each one had a different version. R&D had materials safety sheets, application engineering teams (tech sales/support teams) developed their own version to find solutions for unique client calls, commercialization teams had product descriptions, and customer support teams had a set of specific product details to answer queries. As each team updated its version of the product information, conflicts emerged, making it difficult for gen AI models to use the data. To address this issue, the company is putting all relevant product information in one place.

7. Reuse it or lose it

Reusable code can increase the development speed of generative AI use cases by 30 to 50 percent . 9 Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel, “ A generative AI reset: Rewiring to turn potential into value in 2024 ,” McKinsey, March 4, 2024. But in their haste to make meaningful breakthroughs, teams often focus on individual use cases, which sinks any hope for scale. CIOs need to shift the business’s energies to building transversal solutions that can serve many use cases. In fact, we have found that gen AI high performers are almost three times as likely as their peers to have gen AI foundations built strategically to enable reuse across solutions. 10 McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com.

In committing to reusability, however, it is easy to get caught in building abstract gen AI capabilities that don’t get used, even though, technically, it would be easy to do so. A more effective way to build up reusable assets is to do a disciplined review of a set of use cases, typically three to five, to ascertain their common needs or functions. Teams can then build these common elements as assets or modules that can be easily reused or strung together to create a new capability. Data preprocessing and ingestion, for example, could include a data-chunking mechanism, a structured data-and-metadata loader, and a data transformer as distinct modules. One European bank reviewed which of its capabilities could be used in a wide array of cases and invested in developing a synthesizer module, a translator module, and a sentiment analysis module.

CIOs can’t expect this to happen organically. They need to assign a role, such as the platform owner, and a cross-functional team with a mandate to develop reusable assets for product teams (Exhibit 4), which can include approved tools, code, and frameworks.

The value gen AI could generate is transformational. But capturing the full extent of that value will come only when companies harness gen AI at scale. That requires CIOs to not just acknowledge hard truths but be ready to act on them to lead their business forward.

Aamer Baig is a senior partner in McKinsey’s Chicago office, Douglas Merrill is a partner in the Southern California office, Megha Sinha is a partner in the Bay Area office, Danesha Mead is a consultant in the Denver office, and Stephen Xu is director of product management in the Toronto office.

The authors wish to thank Mani Gopalakrishnan, Mark Gu, Ankur Jain, Rahil Jogani, and Asin Tavakoli for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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