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arXiv Machine Learning By Fangping Lan, Qi Zhang, Eduard Dragut

CATune: Structural Constraint-Aware Bayesian Optimization for DBMS Configuration Tuning

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CATune is a Bayesian optimization framework that incorporates deterministic inter‑knob ordering constraints from DBMS documentation into the search space. By performing optimization within a constraint‑consistent subspace and using a topology‑aware sampling strategy, it avoids costly post‑hoc constraint handling. Experiments on PostgreSQL and MySQL with TPC‑C and TPC‑H workloads show that CATune achieves up to 12.5× faster convergence to the baseline optimum and improves throughput by up to 63.37%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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