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Self-Tuning Query Scheduling for Analytical Workloads

Summary: Presents a lock-free, self-tuning stride scheduler for task-based analytics, replacing OS scheduling with adaptive control of priorities and task granularity. Incorporates domain knowledge to boost scheduling elasticity under concurrent workloads, delivering near-optimal latencies and 10x tail-latency gains over classic DB systems. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6213
Venue
SIGMOD
Year
2021
Pagerank
6.6647555e-05
Overall Rank
4,493 | 69.18%
DOI
10.1145/3448016.3457260

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wagner_sigmod21,
        title = {{Self-Tuning Query Scheduling for Analytical Workloads}},
        author = {Wagner, Benjamin and Kohn, André and Neumann, Thomas},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457260},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457260},
        year = {2021}
}

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