Evaluation of Integrated Frameworks for Optimizing QoS in Serverless Computing

Kumari, Anisha; Sahoo, Bibhudatta; Behera, Ranjan Kumar; Misra, Sanjay; Sharma, Mayank Mohan

doi:10.1007/978-3-030-87007-2_20

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…Some of the authors have also discussed current trends and application of serverless framework. 16,17 Some of the potential works on serverless computing are presented in Table 1.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we have presented some of the existing works which have focused on improving the performance of serverless computing, especially through performance and cost modeling. Some of the authors have also discussed current trends and application of serverless framework 16,17 . Some of the potential works on serverless computing are presented in Table 1.…”

Section: Related Workmentioning

confidence: 99%

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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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“…Some of the authors have also discussed current trends and application of serverless framework. 16,17 Some of the potential works on serverless computing are presented in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Kumari

Sahoo

Behera

2023

Concurrency and Computation

Self Cite

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“…Such delay exceeds 2 sec for applications that uses massive data size [14], [16]. Thus, IoT real-time applications suffer from several failures due to increasing respond time by increasing data size [17]. Another problem of sending huge data size over Internet channels is associated with security and privacy.…”

Section: Introductionmentioning

confidence: 99%

Light weight serverless computing at fog nodes for internet of things systems

Elkholy

Marzok

2022

IJEECS

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Internet of <span lang="EN-US">things (IoT) systems collect large size of data from huge numbers of sensors. A wide rage of IoT systems relies on cloud resources to process and analyze the collected data. However, passing large amount of data to the cloud affects the overall performance and cannot support real-time requirements. Serverless computing is a promising technique that allows developer to write an application code, in any programming language, and specify an event to start its execution. Thus, IoT system can get a good benefit of serverless environment. The proposed work introduces a framework to allow Serverless computing to take place on the Fog nodes near the data collectors. The proposed framework is implemented as an extension to a Kubernetes cluster that manages a set of Docker containers at the fog layer. A prototype of the proposed solution was implemented using Node.Js for coding and YAML files to transfer data. The proposed framework was evaluated against traditional cloud Serverless execution. The experimental results proved the significant enhancement of the framework by dcreasing the respond time especially for data intensive IoT applications.</span>

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