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Unveiling overlooked performance variance in serverless computing

Wen, Jinfeng; Chen, Zhenpeng; Sarro, Federica; Wang, Shangguang; (2025) Unveiling overlooked performance variance in serverless computing. Empirical Software Engineering , 30 (2) , Article 59. 10.1007/s10664-025-10615-3.

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Abstract

Serverless computing is an emerging cloud computing paradigm for developing applications at the function level, known as serverless functions. Due to the highly dynamic execution environment, multiple identical runs of the same serverless function can yield different performance, specifically in terms of end-to-end response latency. However, surprisingly, our analysis of serverless computing-related papers published in top-tier conferences highlights that the research community lacks awareness of the performance variance problem, with only 38.38% of these papers employing multiple runs for quantifying it. To further investigate, we analyze the performance of 72 serverless functions collected from these papers. Our findings reveal that the performance of these serverless functions can differ by up to 338.76% (44.28% on average) across different runs. Moreover, 61.11% of these functions produce unreliable performance results, with a low number of repetitions commonly employed in the serverless computing literature. Our study highlights a lack of awareness in the serverless computing community regarding the well-known performance variance problem in software engineering. The empirical results illustrate the substantial magnitude of this variance, emphasizing that ignoring the variance can affect research reproducibility and result reliability.

Type: Article
Title: Unveiling overlooked performance variance in serverless computing
DOI: 10.1007/s10664-025-10615-3
Publisher version: https://doi.org/10.1007/s10664-025-10615-3
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Serverless computing; Performance variance; Empirical study; Cloud computing
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10204577
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