MAFF: Self-adaptive Memory Optimization for Serverless Functions

Tetiana Zubko, Anshul Jindal, Mohak Chadha, Michael Gerndt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Function-as-a-Service (FaaS), a key enabler of serverless computing, has been proliferating, as it offers a cheap alternative for application development and deployment. However, while offering many advantages, FaaS also poses new challenges. In particular, most commercial FaaS providers still require users to manually configure the memory allocated to the FaaS functions based on their experience and knowledge. This often leads to suboptimal function performance and higher execution costs. In this paper, we present a framework called MAFF that automatically finds the optimal memory configurations for the FaaS functions based on two optimization objectives: cost-only and balanced (balance between cost and execution duration). Furthermore, MAFF self-adapts the memory configurations for the FaaS functions based on the changing function inputs or other requirements, such as an increase in the number of requests. Moreover, we propose and implement different optimization algorithms for different objectives. We demonstrate the functionality of MAFF on AWS Lambda by testing on four different categories of FaaS functions. Our results show that the suggested memory configurations with the Linear algorithm achieve 90% accuracy with a speedup of 2x compared to the other algorithms. Finally, we compare MAFF with two popular memory optimization tools provided by AWS, i.e., AWS Compute Optimizer and AWS Lambda Power Tuning, and demonstrate how our framework overcomes their limitations.

Original languageEnglish
Title of host publicationService-Oriented and Cloud Computing - 9th IFIP WG 6.12 European Conference, ESOCC 2022, Proceedings
EditorsFabrizio Montesi, George Angelos Papadopoulos, Wolf Zimmermann
PublisherSpringer Science and Business Media Deutschland GmbH
Pages137-154
Number of pages18
ISBN (Print)9783031047176
DOIs
StatePublished - 2022
Event9th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2022 - Wittenberg, Germany
Duration: 22 Mar 202224 Mar 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2022
Country/TerritoryGermany
CityWittenberg
Period22/03/2224/03/22

Keywords

  • Function-as-a-Service
  • cost optimization
  • duration optimization
  • memory allocation
  • memory optimization
  • serverless

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