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Configuration knobs of database systems are essential to achieve highthroughput and low latency. Recently, automatic tuning systems using machinelearning methods (ML) have shown to find better configurations compared toexperienced database administrators (DBAs). However, there are still gaps toapply the existing systems in production environments, especially in the cloud.First, they conduct tuning for a given workload within a limited time windowand ignore the dynamicity of workloads and data. Second, they rely on a copiedinstance and do not consider the availability of the database when samplingconfigurations, making the tuning expensive, delayed, and unsafe. To fill thesegaps, we propose OnlineTune, which tunes the online databases safely inchanging cloud environments. To accommodate the dynamicity, OnlineTune embedsthe environmental factors as context feature and adopts contextual BayesianOptimization with context space partition to optimize the database adaptivelyand scalably. To pursue safety during tuning, we leverage the black-box and thewhite-box knowledge to evaluate the safety of configurations and propose a safeexploration strategy via subspace adaptation.%, greatly decreasing the risks ofapplying bad configurations. We conduct evaluations on dynamic workloads frombenchmarks and real-world workloads. Compared with the state-of-the-artmethods, OnlineTune achieves 14.4%~165.3% improvement on cumulative performancewhile reducing 91.0%~99.5% unsafe configuration recommendations.

Towards Dynamic and Safe Configuration Tuning for Cloud Databases