ESTune: Bayesian Uncertainty-Guided Early Stopping for Database Configuration Tuning
Summary: ESTune accelerates database knob tuning by early-stopping clearly poor configurations, replacing full-workload evaluation with high-confidence performance estimates from partial runs. Core novelty: a hybrid Bayesian NN plus MAML few-shot/meta-learning to predict configuration performance uncertainty and preserve tuning quality. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Zhongwei Yue (Henan Normal University)
- 2. Jun-Peng Zhu (East China Normal University)
- 3. Peng Cai (East China Normal University)
- 4. Xuan Zhou (East China Normal University)
- 5. Quanqing Xu (Ant Financial)
- 6. Chuanhui Yang (Ant Financial)
BibTeX Citation
@inproceedings{yue_sigmod26,
title = {{ESTune: Bayesian Uncertainty-Guided Early Stopping for Database Configuration Tuning}},
author = {Yue, Zhongwei and Zhu, Jun-Peng and Cai, Peng and Zhou, Xuan and Xu, Quanqing and Yang, Chuanhui},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786649},
url = {https://dl.acm.org/doi/10.1145/3786649},
year = {2026}
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