Cold-Start Anti-Patterns and Refactorings in Serverless Systems: An Empirical Study
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| author | Tariq, Syed Salauddin Mohammad Hassan, Foyzul Bosu, Amiangshu Roy, Probir |
| author_facet | Tariq, Syed Salauddin Mohammad Hassan, Foyzul Bosu, Amiangshu Roy, Probir |
| contents | Serverless computing simplifies deployment and scaling, yet cold-start latency remains a major performance bottleneck. Unlike prior work that treats mitigation as a black-box optimization, we study cold starts as a developer-visible design problem. From 81 adjudicated issue reports across open-source serverless systems, we derive taxonomies of initialization anti-patterns, remediation strategies, and diagnostic challenges spanning design, packaging, and runtime layers. Building on these insights, we introduce SCABENCH, a reproducible benchmark, and INITSCOPE, a lightweight analysis framework linking what code is loaded with what is executed. On SCABENCH, INITSCOPE improved localization accuracy by up to 40% and reduced diagnostic effort by 64% compared with prior tools, while a developer study showed higher task accuracy and faster diagnosis. Together, these results advance evidence-driven, performance-aware practices for cold-start mitigation in serverless design. Availability: The research artifact is publicly accessible for future studies and improvements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cold-Start Anti-Patterns and Refactorings in Serverless Systems: An Empirical Study Tariq, Syed Salauddin Mohammad Hassan, Foyzul Bosu, Amiangshu Roy, Probir Distributed, Parallel, and Cluster Computing Serverless computing simplifies deployment and scaling, yet cold-start latency remains a major performance bottleneck. Unlike prior work that treats mitigation as a black-box optimization, we study cold starts as a developer-visible design problem. From 81 adjudicated issue reports across open-source serverless systems, we derive taxonomies of initialization anti-patterns, remediation strategies, and diagnostic challenges spanning design, packaging, and runtime layers. Building on these insights, we introduce SCABENCH, a reproducible benchmark, and INITSCOPE, a lightweight analysis framework linking what code is loaded with what is executed. On SCABENCH, INITSCOPE improved localization accuracy by up to 40% and reduced diagnostic effort by 64% compared with prior tools, while a developer study showed higher task accuracy and faster diagnosis. Together, these results advance evidence-driven, performance-aware practices for cold-start mitigation in serverless design. Availability: The research artifact is publicly accessible for future studies and improvements. |
| title | Cold-Start Anti-Patterns and Refactorings in Serverless Systems: An Empirical Study |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2512.16066 |