Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions
Summary: Centrum is a model-based DBMS auto-tuner using two-phase boosting ensembles with distribution-free interval estimation via conformal prediction. Fuse gradient boosting with conformal inference in BO; it outperforms 21 SOTA tuners on two DBMSs across three workloads. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yuanhao Lai (Huawei)
- 2. Pengfei Zheng (Huawei)
- 3. Chenpeng Ji (Huawei)
- 4. Yan Li (Huawei)
- 5. Songhan Zhang (Chinese University of Hong Kong; Huawei)
- 6. Rutao Zhang (Huawei)
- 7. Zhengang Wang (Huawei)
- 8. Yunfei Du (Huawei)
BibTeX Citation
@inproceedings{lai_sigmod25,
title = {{Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions}},
author = {Lai, Yuanhao and Zheng, Pengfei and Ji, Chenpeng and Li, Yan and Zhang, Songhan and Zhang, Rutao and Wang, Zhengang and Du, Yunfei},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709671},
url = {https://dl.acm.org/doi/10.1145/3709671},
year = {2025}
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