Towards Demystifying Intra-Function Parallelism in Serverless Computing

Michael Kiener, Mohak Chadha, Michael Gerndt

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

8 Scopus citations

Abstract

Serverless computing offers a pay-per-use model with high elasticity and automatic scaling for a wide range of applications. Since cloud providers abstract most of the underlying infrastructure, these services work similarly to black-boxes. As a result, users can influence the resources allocated to their functions, but might not be aware that they have to parallelize them to profit from the additionally allocated virtual CPUs (vCPUs). In this paper, we analyze the impact of parallelization within a single function and container instance for AWS Lambda, Google Cloud Functions (GCF), and Google Cloud Run (GCR). We focus on compute-intensive workloads since they benefit greatly from parallelization. Furthermore, we investigate the correlation between the number of allocated CPU cores and vCPUs in serverless environments. Our results show that the number of available cores to a function/container instance does not always equal the number of allocated vCPUs. By parallelizing serverless workloads, we observed cost savings up to 81% for AWS Lambda, 49% for GCF, and 69.8% for GCR.

Original languageEnglish
Title of host publicationProceedings of the 7th International Workshop on Serverless Computing, WoSC 2021
PublisherAssociation for Computing Machinery, Inc
Pages42-49
Number of pages8
ISBN (Electronic)9781450391726
DOIs
StatePublished - 6 Dec 2021
Event7th International Workshop on Serverless Computing, WoSC 2021 - Virtual, Online, Canada
Duration: 6 Dec 2021 → …

Publication series

NameProceedings of the 7th International Workshop on Serverless Computing, WoSC 2021

Conference

Conference7th International Workshop on Serverless Computing, WoSC 2021
Country/TerritoryCanada
CityVirtual, Online
Period6/12/21 → …

Keywords

  • Container-as-a-Service
  • Cost
  • Function-as-a-Service
  • Parallel execution
  • Parallelization
  • Performance
  • Serverless

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