Klink: Progress-Aware Scheduling for Streaming Data Systems
Summary: Leveraging watermarks to infer stream progress and window deadlines, Klink schedules pipelines to unblock windows, speed propagation. Runs on Apache Flink; shows gains over schedulers for scale-up/scale-out workloads with memory-aware execution. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Omar Farhat (University of Waterloo)
- 2. Khuzaima Daudjee (University of Waterloo)
- 3. Leonardo Querzoni (Sapienza University)
BibTeX Citation
@inproceedings{farhat_sigmod21,
title = {{Klink: Progress-Aware Scheduling for Streaming Data Systems}},
author = {Farhat, Omar and Daudjee, Khuzaima and Querzoni, Leonardo},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452794},
url = {https://dl.acm.org/doi/10.1145/3448016.3452794},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,368 | Enjima: A Resource-Adaptive Stream Processing System | 2026 | SIGMOD | 5.093636e-05 |
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