SeBS-Flow: Benchmarking Serverless Cloud Function Workflows

Serverless computing has emerged as a prominent paradigm, with a significant adoption rate among cloud customers. While this model offers advantages such as abstraction from the deployment and resource scheduling, it also poses limitations in handling complex use cases due to the restricted nature of individual functions. Serverless workflows address this limitation by orchestrating multiple functions into a cohesive application. However, existing serverless workflow platforms exhibit significant differences in their programming models and infrastructure, making fair and consistent performance evaluations difficult in practice. To address this gap, we propose the first serverless workflow benchmarking suite SeBS-Flow, providing a platform-agnostic workflow model that enables consistent benchmarking across various platforms. SeBS-Flow includes six real-world application benchmarks and four microbenchmarks representing different computational patterns. We conduct comprehensive evaluations on three major cloud platforms, assessing performance, cost, scalability, and runtime deviations. We make our benchmark suite open-source, enabling rigorous and comparable evaluations of serverless workflows over time. Implementation: https://github.com/spcl/serverless-benchmarks Artifact: https://github.com/spcl/sebs-flow-artifact

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Publication Details

Published
2025-03-26
DOI
https://doi.org/10.1145/3689031.3717465
Citations
5
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
16.23
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article

SeBS-Flow: Benchmarking Serverless Cloud Function Workflows

Torsten Hoefler, Larissa Schmid, Alexandru Calotoiu, Anne Koziolek et al.
5 citations
Cloud Computing and Resource Management
16.23
article

SeBS-Flow: Benchmarking Serverless Cloud Function Workflows

Torsten Hoefler, Larissa Schmid, Alexandru Calotoiu, Anne Koziolek, Marcin Copik, Laurin Brandner
article en
5 citations

Abstract

Serverless computing has emerged as a prominent paradigm, with a significant adoption rate among cloud customers. While this model offers advantages such as abstraction from the deployment and resource scheduling, it also poses limitations in handling complex use cases due to the restricted nature of individual functions. Serverless workflows address this limitation by orchestrating multiple functions into a cohesive application. However, existing serverless workflow platforms exhibit significant differences in their programming models and infrastructure, making fair and consistent performance evaluations difficult in practice. To address this gap, we propose the first serverless workflow benchmarking suite SeBS-Flow, providing a platform-agnostic workflow model that enables consistent benchmarking across various platforms. SeBS-Flow includes six real-world application benchmarks and four microbenchmarks representing different computational patterns. We conduct comprehensive evaluations on three major cloud platforms, assessing performance, cost, scalability, and runtime deviations. We make our benchmark suite open-source, enabling rigorous and comparable evaluations of serverless workflows over time. Implementation: https://github.com/spcl/serverless-benchmarks Artifact: https://github.com/spcl/sebs-flow-artifact

Karlsruhe Institute of Technology (DE), ETH Zurich (CH)
Openalex Percentile: Top 1%
Cloud Computing and Resource Management
16.23
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