Parrotfish: Parametric Regression for Optimizing Serverless Functions
Authors: 
Arshia Moghimi, Joe Hattori, Alexander Li, Mehdi Ben Chikha, 
Mohammad ShahradAuthors Info & Claims
Pages 177 - 192
Abstract
Serverless computing is a new paradigm that aims to remove the burdens of cloud management from developers. Yet rightsizing serverless functions remains a pain point for developers. Choosing the right memory configuration is necessary to ensure cost and/or performance optimality for serverless workloads. In this work, we identify that using parametric regression can significantly simplify function rightsizing compared to black-box optimization techniques currently available. With this insight, we build a tool, called Parrotfish, which finds optimal configurations through an online learning process. It also allows users to communicate constraints on execution time, or to relax cost optimality to gain performance. Parrotfish achieves substantially lower exploration costs (1.81-9.96×) compared with the state-of-the-art tools, while delivering similar or better recommendations.
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Index Terms
- Parrotfish: Parametric Regression for Optimizing Serverless Functions
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October 2023
624 pages
ISBN:9798400703874
DOI:10.1145/3620678
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