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Spur: Mitigating Slow Instances in Large-Scale Streaming Pipelines

Summary: A 3500-container streaming pipeline with SLO on tail latency contends with co-tenant load, stalls, and resource delays. Spur detects slow instances with pruning to drive speculative replication, yielding 10–38% p99 gains using 0.5% extra resources. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5964
Venue
SIGMOD
Year
2020
Pagerank
5.4560857e-05
Overall Rank
8,309 | 43.00%
DOI
10.1145/3318464.3386142

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod20,
        title = {{Spur: Mitigating Slow Instances in Large-Scale Streaming Pipelines}},
        author = {Wang, Ke and Floratou, Avrilia and Agrawal, Ashvin and Musgrave, Daniel},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3386142},
        url = {https://dl.acm.org/doi/10.1145/3318464.3386142},
        year = {2020}
}

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