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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2305.04309 |
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| _version_ | 1866910779836465152 |
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| author | Wen, Jinfeng Chen, Zhenpeng Sarro, Federica Wang, Shangguang |
| author_facet | Wen, Jinfeng Chen, Zhenpeng Sarro, Federica Wang, Shangguang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_04309 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unveiling Overlooked Performance Variance in Serverless Computing Wen, Jinfeng Chen, Zhenpeng Sarro, Federica Wang, Shangguang Software Engineering 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. |
| title | Unveiling Overlooked Performance Variance in Serverless Computing |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2305.04309 |