The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Sparrow: distributed, low latency scheduling
Open accessAbstract
Large-scale data analytics frameworks are shifting towards shorter task durations and larger degrees of parallelism to provide low latency. Scheduling highly parallel jobs that complete in hundreds of milliseconds poses a major challenge for task schedulers, which will need to schedule millions of tasks per second on appropriate machines while offering millisecond-level latency and high availability. We demonstrate that a decentralized, randomized sampling approach provides near-optimal performance while avoiding the throughput and availability limitations of a centralized design. We implement and deploy our scheduler, Sparrow, on a 110-machine cluster and demonstrate that Sparrow performs within 12% of an ideal scheduler.
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Cites
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Analysis and simulation of a fair queueing algorithm
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Analysis and simulation of a fair queueing algorithm
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Self-citationSLURM: Simple Linux Utility for Resource Management
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Cited by
Large-scale cluster management at Google with Borg
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015) · Proceedings of the Tenth European Conference on Computer Systems · doi:10.1145/2741948.2741964 · Cited by 1,468
Gan, Y., Zhang, Y., Cheng, D., Shetty, A., Rathi, P., Katarki, N., Bruno, A., Hu, J., Ritchken, B., Jackson, B., et al. (2019) · Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3297858.3304013 · Cited by 665
Splitwise: Efficient Generative LLM Inference Using Phase Splitting
Patel, P., Choukse, E., Zhang, C., Shah, A., Goiri, Í., Maleki, S., & Bianchini, R. (2024) · 2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca59077.2024.00019 · Cited by 298
“Insights from prior work on partitioned or replicated scheduling could help improve scalability [27], [61], [72] and are orthogonal to Splitwise.”
BackgroundLAVEA: latency-aware video analytics on edge computing platform
Yi, S., Hao, Z., Zhang, Q., Zhang, Q., Shi, W., & Li, Q. (2017) · Proceedings of the Second ACM/IEEE Symposium on Edge Computing · doi:10.1145/3132211.3134459 · Cited by 244
The evolution of distributed computing systems: from fundamental to new frontiers
Lindsay, D., Gill, S. S., Smirnova, D., & Garraghan, P. (2021) · Computing · doi:10.1007/s00607-020-00900-y · Cited by 45
Metadata
- Authors
- Ousterhout, K., Wendell, P., Zaharia, M., & Stoica, I.
- Venue
- Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
- Publisher
- ACM
- Published
- 2013-11-03
- Pages
- 69-84
- OpenAlex
- W2102849319
- Cited by
- 586
- References
- 22
- FWCI
- 40.36
- Type
- conference paper
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: Sparrow: distributed, low latency scheduling. https://app.cochat.ai/papers/10.1145/2517349.2522716
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- Ousterhout, K., Wendell, P., Zaharia, M., & Stoica, I. (2013). Sparrow: distributed, low latency scheduling. Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. https://doi.org/10.1145/2517349.2522716
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
