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Characterizing Serverless Workloads: A Large-Scale Analysis of Function-as-a-Service Platforms — arXiv:2611.05081

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05081
License (as archived): CC0-1.0
Archived: 2026-11-04T12:34:51.997Z
SHA-256 of the archived text: 8b5bdc34c23c22e24525fe213a0affac75f0bc3e047edb06f07f805614148acd
Stable URL of this page: https://metraipolis.com/documento/dc_dcbc2dc52153

How to cite

Characterizing Serverless Workloads: A Large-Scale Analysis of Function-as-a-Service Platforms — arXiv:2611.05081 — arXiv (en), https://arxiv.org/abs/2611.05081 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dcbc2dc52153 (Metraipolis archive dc_dcbc2dc52153, sha256 8b5bdc34c23c22e24525fe213a0affac75f0bc3e047edb06f07f805614148acd)

BibTeX

@misc{dcdcbc2dc52153,
  title = {Characterizing Serverless Workloads: A Large-Scale Analysis of Function-as-a-Service Platforms — arXiv:2611.05081},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05081},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dcbc2dc52153; sha256 8b5bdc34c23c22e24525fe213a0affac75f0bc3e047edb06f07f805614148acd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05081. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Serverless computing is a popular approach to application deployment without having to manage the underlying infrastructure. Its most common implementation is Function-as-a-Service (FaaS), in which applications are structured as fine-grained functions. FaaS providers face non-trivial tasks in the management of their platforms, including cold starts, auto-scaling, and scheduling. The design of optimized platforms requires an accurate understanding of realistic serverless workloads. However, existing research and platforms rely on insights from single, often outdated datasets. In this work, we conduct a large-scale, cross-platform comparative analysis of eight publicly available FaaS workload traces from major cloud providers (Alibaba, Azure, Huawei, IBM) and a scientific computing platform (Globus). We provide a comprehensive and up-to-date view of the serverless ecosystem by characterizing key dimensions such as invocation patterns, latency, cold start behavior, and resource utilization. Our analysis shows that although invocation patterns remain universal, execution times have shortened, shifting the performance bottleneck toward platform scheduling and dependency management. We also observe an architectural shift toward "Serverless 2.0" models, where concurrent, long-lived containers are increasingly utilized to bypass cold start penalties. Furthermore, we found resource over-provisioning and extreme skew in user request volumes. These findings provide actionable insights for designing more efficient FaaS platforms, informing improved approaches to targeted cold start mitigation, predictive resource management, and dependency-aware scheduling.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv cs.DC, nuovi lavori del giorno).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC) ; Performance (cs.PF).
Pagina del lavoro: https://arxiv.org/abs/2611.05081 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T12:27:17.014Z dalla pagina https://arxiv.org/list/cs.DC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).