An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning
Summary: CDBTune: end-to-end automatic cloud DB tuning with deep RL (DDPG) for high-dimensional continuous knobs. Sample-efficient, reward-driven online tuning adapts to hardware/workload shifts and beats state-of-the-art tuners and DBAs on real cloud workloads. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ji Zhang (Huazhong University of Science and Technology)
- 2. Yu Liu (Huazhong University of Science and Technology)
- 3. Ke Zhou (Huazhong University of Science and Technology)
- 4. Guoliang Li (Tsinghua University)
- 5. Zhili Xiao (Tencent)
- 6. Bin Cheng (Tencent)
- 7. Jiashu Xing (Tencent)
- 8. Yangtao Wang (Huazhong University of Science and Technology)
- 9. Tianheng Cheng (Huazhong University of Science and Technology)
- 10. Li Liu (Huazhong University of Science and Technology)
- 11. Minwei Ran (Huazhong University of Science and Technology)
- 12. Zekang Li (Huazhong University of Science and Technology)
BibTeX Citation
@inproceedings{zhang_sigmod19,
title = {{An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning}},
author = {Zhang, Ji and Liu, Yu and Zhou, Ke and Li, Guoliang and Xiao, Zhili and Cheng, Bin and Xing, Jiashu and Wang, Yangtao and Cheng, Tianheng and Liu, Li and Ran, Minwei and Li, Zekang},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3300085},
url = {https://dl.acm.org/doi/10.1145/3299869.3300085},
year = {2019}
}
Incoming Citations (Sorted by Pagerank)
Showing 50 of 100 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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