Serverless Computing: A Survey of Opportunities, Challenges, and Applications

Shafiei, Hossein; Khonsari, Ahmad; Mousavi, Payam

doi:10.1145/3510611

Cited by 96 publications

(40 citation statements)

References 108 publications

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“…They compared serverless computing against traditional cloud computing, highlighting its benefits and application areas. Shafiei et al [5] categorized serverless applications into eight domains, including scientific computing, the internet of things, and artificial intelligence, among others. They discussed migration objectives, assessment results, and challenges for each domain.…”

Section: Related Reviewmentioning

confidence: 99%

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Jawaddi

Ismail

2023

Preprint

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The popularity of serverless computing has been fueled by its operational simplicity, pay-per-use pricing model, and the ability to autoscale. However, there is a lack of comprehensive reviews that focus on the autoscaling context in serverless computing. In this paper, we address this gap by proposing a taxonomy of autoscaling properties for serverless computing. To gather relevant information, we review recent contributions on autoscaling in serverless computing from 2018 to 2022. Using the proposed taxonomy, we analyze the existing autoscaling solutions. Our analysis reveals that the scaling objectives explored by researchers are limited to certain elements, and the existing serverless autoscaling approaches do not provide guarantees that the scaling policies or strategies can meet the Service Level Agreement (SLA) requirements. We conclude by recommending open challenges from three perspectives: verification of autoscaling, energy-driven autoscaling, and anomaly-aware autoscaling. These challenges highlight the need for future research to address the limitations of existing autoscaling approaches and provide more robust and reliable autoscaling mechanisms for serverless computing.

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Section: Related Reviewmentioning

confidence: 99%

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Jawaddi

Ismail

2023

Preprint

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“…To relieve customers from cloud management duties, Amazon first introduced Amazon Lambda in 2014 after which big CSPs like Google and Microsoft adopted in 2016. Amazon Lambda is an example of "serverless cloud service" or "Function as a Service" (FaaS) which offers a pay-as-you-go model, and service auto-scale feature [2]. Customers only need to register their functions with the CSPs.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to other cloud service models, the serverless model is more affordable due to resource multiplexing and infrastructure heterogeneity [2]. For example, Deloitte [3] reported that Amazon serverless offers the same capabilities as Amazon EC2 but only with 65 percent of the cost.…”

Section: Introductionmentioning

confidence: 99%

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Managing Cold-start in The Serverless Cloud with Temporal Convolutional Networks

Tn¹

2023

Preprint

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Serverless cloud is an innovative cloud service model that frees customers from most cloud management duties. It also offers the same advantages as other cloud models but at much lower costs. As a result, the serverless cloud has been increasingly employed in high-impact areas such as system security, banking, and health care. A big threat to the serverless cloud's performance is cold-start, which is when the time of provisioning the needed cloud resource to serve customers' requests incurs unacceptable costs to the service providers and/or the customers.This paper proposes a novel low-coupling, high-cohesion ensemble policy that addresses the cold-start problem at infrastructure- and function-levels of the serverless cloud stack, while the state of the art policies have a more narrowed focus. This ensemble policy anchors on the prediction of function instance arrivals, 10 to 15 minutes into the future. It is achievable by using the temporal convolutional network (TCN) deep-learning method. Bench-marking results on a real-world dataset from a large-scale serverless cloud provider show that TCN out-performs other popular machine learning algorithms for time series. Going beyond cold-start management, the proposed policy and publicly available codes can be adopted in solving other cloud problems such as optimizing the provisioning of virtual software-defined network assets.

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“…In serverless computing, almost all the operational responsibilities, such as infrastructure management, provisioning of computing resources, system configuration, and scaling up and down of on-demand services, are migrated from the client to the service provider side. 1,2 Developers can concentrate more on writing business logic at the front end without worrying about the resource provisioning and configuration of the underlying infrastructure. This leads to better productivity in application development.…”

Section: Introductionmentioning

confidence: 99%

Workflow aware analytical model to predict performance and cost of serverless execution

Kumari

Sahoo

Behera

2023

Concurrency and Computation

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SummaryServerless computing has emerged as a powerful deployment model based on the Function‐as‐a‐Service (FaaS) paradigm, where applications are orchestrated through a set of independent functions. The function orchestration within an application can be represented through a serverless workflow, which defines the overall execution plan of the application. To ensure the quality of service for serverless computing platforms, it is essential to develop performance and cost models that can predict the service quality that can be obtained from deploying and executing applications in the cloud platform. While several analytical models have been developed for various cloud deployment frameworks in recent years, there has been a lack of performance and cost analysis models for serverless computing platforms. The existing performance and cost monitoring tools available in serverless frameworks face several challenges, such as complexity, lack of transparency, and incomplete monitoring data. In this paper, we fill the gap by proposing an efficient workflow‐based analytical model that can estimate the end‐to‐end response time and cost of the serverless execution plan. The proposed model can handle complex structures like loop, cycles, self‐loop, and parallel substructures that exist in serverless workflows. Additionally, we propose a heuristic optimization algorithm to identify the optimal resource configuration to achieve the optimal response time under a given budget constraint. We evaluated the effectiveness of the proposed model by considering seven serverless applications in both AWS Lambda and Microsoft Azure platforms. We compared the accuracy of the proposed model with the real values of response time and cost obtained in AWS Lambda and Microsoft Azure serverless platforms. The proposed performance and cost model in the AWS Lambda platform has been observed to have an average accuracy of 99.2% and 98.7% respectively. In the Microsoft Azure platform, the average accuracy of the performance and cost model has been observed to be 98.6% and 98.2% respectively.

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Serverless Computing: A Survey of Opportunities, Challenges, and Applications

Cited by 96 publications

References 108 publications

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Managing Cold-start in The Serverless Cloud with Temporal Convolutional Networks

Workflow aware analytical model to predict performance and cost of serverless execution

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