Paper Info
Title
Modeling and Optimization of Performance and Cost of Serverless Applications
Abstract
Function-as-a-Service (FaaS) and serverless applications have proliferated significantly in recent years because of their high scalability, ease of resource management, and pay-as-you-go pricing model. However, cloud users are facing practical problems when they migrate their applications to the serverless pattern, which are the lack of analytical performance and billing model and the trade-off between limited budget and the desired quality of service of serverless applications. In this article, we fill this gap by proposing and answering two research questions regarding the prediction and optimization of performance and cost of serverless applications. We propose a new construct to formally define a serverless application workflow, and then implement analytical models to predict the average end-to-end response time and the cost of the workflow. Consequently, we propose a heuristic algorithm named Probability Refined Critical Path Greedy algorithm (PRCP) with four greedy strategies to answer two fundamental optimization questions regarding the performance and the cost. We extensively evaluate the proposed models by conducting experimentation on AWS Lambda and Step Functions. Our analytical models can predict the performance and cost of serverless applications with more than 98 percent accuracy. The PRCP algorithms can achieve the optimal configurations of serverless applications with 97 percent accuracy on average.
Year
DOI
Venue
2021
10.1109/TPDS.2020.3028841
IEEE Transactions on Parallel and Distributed Systems
Keywords
DocType
Volume
Cloud serverless computing,performance modeling,performance optimization,cost modeling,cost optimization
Journal
32
Issue
ISSN
Citations 
3
1045-9219
5
PageRank 
References 
Authors
0.45
0
2
Name
Order
Citations
PageRank
Changyuan Lin151.13
Hamzeh Khazaei222317.82