HotHash: Hotness-Aware Consistent Hashing for Cloud Databases
Summary: HotHash combines hotness-proportional range hashing with per-segment randomized virtual rings to preserve locality while balancing skewed workloads. It retains consistent-hashing robustness to node changes and improves average and tail latency by 1.4–150×. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Junyong Zhao (University of Arizona)
- 2. Jia Yuan (University of Arizona)
- 3. Zui Chen (Massachusetts Institute of Technology)
- 4. Samuel Madden (Massachusetts Institute of Technology)
- 5. Lei Cao (University of Arizona)
BibTeX Citation
@inproceedings{zhao_sigmod26,
title = {{HotHash: Hotness-Aware Consistent Hashing for Cloud Databases}},
author = {Zhao, Junyong and Yuan, Jia and Chen, Zui and Madden, Samuel and Cao, Lei},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802073},
url = {https://dl.acm.org/doi/10.1145/3802073},
year = {2026}
}
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