Serverless in the wild: Characterizing and optimizing the serverless workload at a large cloud provider

Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, Ricardo Bianchini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

Function as a Service (FaaS) has been gaining popularity as a way to deploy computations to serverless backends in the cloud. This paradigm shifts the complexity of allocating and provisioning resources to the cloud provider, which has to provide the illusion of always-available resources (i.e., fast function invocations without cold starts) at the lowest possible resource cost. Doing so requires the provider to deeply understand the characteristics of the FaaS workload. Unfortunately, there has been little to no public information on these characteristics. Thus, in this paper, we first characterize the entire production FaaS workload of Azure Functions. We show for example that most functions are invoked very infrequently, but there is an 8-order-of-magnitude range of invocation frequencies. Using observations from our characterization, we then propose a practical resource management policy that significantly reduces the number of function cold starts, while spending fewer resources than state-of-the-practice policies.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 USENIX Annual Technical Conference, ATC 2020
PublisherUSENIX Association
Pages205-218
Number of pages14
ISBN (Electronic)9781939133144
StatePublished - 2020
Event2020 USENIX Annual Technical Conference, ATC 2020 - Virtual, Online
Duration: Jul 15 2020Jul 17 2020

Publication series

NameProceedings of the 2020 USENIX Annual Technical Conference, ATC 2020

Conference

Conference2020 USENIX Annual Technical Conference, ATC 2020
CityVirtual, Online
Period7/15/207/17/20

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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