WAter: A Workload-Adaptive Knob Tuning System based on Workload Compression
Selecting appropriate values for the configurable parameters of Database Management Systems (DBMS) to improve performance is a significant challenge. Recent machine learning (ML)-based tuning systems have shown strong potential, but their practical adoption is often limited by the high tuning cost. This cost arises from two main factors: (1) the system needs to evaluate a large number of configurations to identify a satisfactory one, and (2) for each configuration, the system must execute the entire target workload on the DBMS, which is both time-consuming. Existing studies have primarily addressed the first factor by improving sample efficiency, that is, by reducing the number of configu...
| Authors | Yibo Wang, Jiale Lao, Chen Zhang et al. |
|---|---|
| Primary category | cs.DB |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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