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CLASP: Chained-Request-Aware Scaling and Operator Placement for Serverless Stream Processing — arXiv:2608.29103

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29103
License (as archived): CC0-1.0
Archived: 2026-10-17T08:19:01.471Z
SHA-256 of the archived text: aabb4bc632db8c8945acefc60c3aba79774d8479b217cb55c9911f8a7009fa5a
Stable URL of this page: https://metraipolis.com/documento/dc_a4e94192fdcc

How to cite

CLASP: Chained-Request-Aware Scaling and Operator Placement for Serverless Stream Processing — arXiv:2608.29103 — arXiv (en), https://arxiv.org/abs/2608.29103 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a4e94192fdcc (Metraipolis archive dc_a4e94192fdcc, sha256 aabb4bc632db8c8945acefc60c3aba79774d8479b217cb55c9911f8a7009fa5a)

BibTeX

@misc{dca4e94192fdcc,
  title = {CLASP: Chained-Request-Aware Scaling and Operator Placement for Serverless Stream Processing — arXiv:2608.29103},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29103},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a4e94192fdcc; sha256 aabb4bc632db8c8945acefc60c3aba79774d8479b217cb55c9911f8a7009fa5a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29103. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Stateful serverless (Function-as-a-Service) environments, whose workers host state servers, are increasingly used for stream processing. A stream application is a pipeline of operators, where each operator forwards intermediate data downstream through a chained request. As input rates fluctuate, the system should adjust operator parallelism and place instances across workers to sustain the incoming rate. Existing approaches do so without fully accounting for chained-request overhead, leading them to misestimate the required number of workers. Too few leave the cluster unable to keep up with the input rate, while too many route a larger fraction of chained requests across worker boundaries, increasing end-to-end latency. We propose CLASP, a scaling and scheduling strategy for stream processing in stateful serverless environments. At runtime, CLASP estimates execution cost and chained-request cost from observed metrics. Under a capacity model that covers the two costs, it adjusts operator parallelism and packs operators onto the fewest workers that can sustain the target input rate. Once a scaling decision is made, CLASP migrates each operator's state together with its instances, thereby minimizing execution pause time. Experiments show that CLASP improves throughput by up to 3.3x and reduces median end-to-end latency by up to 76% compared with state-of-the-art scaling strategies.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-01 in cs.DC).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2608.29103 — 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 2026-10-17T08:07:11.113Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).