Budget-aware Index Tuning with Reinforcement Learning
Summary: Budget-aware index tuning under a capped what-if budget; models the search as an MDP to balance exploration and exploitation. Applies Monte Carlo Tree Search for RL-guided index configuration, outperforming budget-aware baselines on benchmarks and real workloads. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Wentao Wu (Microsoft)
- 2. Chi Wang (Microsoft)
- 3. Tarique Siddiqui (Microsoft)
- 4. Junxiong Wang (Cornell University)
- 5. Vivek Narasayya (Microsoft)
- 6. Surajit Chaudhuri (Microsoft)
- 7. Philip A. Bernstein (Microsoft)
BibTeX Citation
@inproceedings{wu_sigmod22,
title = {{Budget-aware Index Tuning with Reinforcement Learning}},
author = {Wu, Wentao and Wang, Chi and Siddiqui, Tarique and Wang, Junxiong and Narasayya, Vivek and Chaudhuri, Surajit and Bernstein, Philip A.},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526128},
url = {https://dl.acm.org/doi/10.1145/3514221.3526128},
year = {2022}
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