HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements
Summary: HUNTER is an online, hybrid tuner for cloud DBs addressing personalized constraints and workload heterogeneity. It uses GA-warmed samples to bootstrap deep RL, with PCA, Random Forest, and a Fast Exploration Strategy to prune the search space, plus clone-based parallel testing for fast, safe online tuning; up to 2.8x (1 clone) and 22.8x (20 clones) speedups. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Baoqing Cai (Huazhong University of Science and Technology)
- 2. Yu Liu (Huazhong University of Science and Technology)
- 3. Ce Zhang (ETH Zurich)
- 4. Guangyu Zhang (Huazhong University of Science and Technology)
- 5. Ke Zhou (Huazhong University of Science and Technology)
- 6. Li Liu (Huazhong University of Science and Technology)
- 7. Chunhua Li (Huazhong University of Science and Technology)
- 8. Bin Cheng (Tencent)
- 9. Jie Yang (Tencent)
- 10. Jiashu Xing (Tencent)
BibTeX Citation
@inproceedings{cai_sigmod22,
title = {{HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements}},
author = {Cai, Baoqing and Liu, Yu and Zhang, Ce and Zhang, Guangyu and Zhou, Ke and Liu, Li and Li, Chunhua and Cheng, Bin and Yang, Jie and Xing, Jiashu},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517882},
url = {https://dl.acm.org/doi/10.1145/3514221.3517882},
year = {2022}
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