#configuration-tuning

#configuration-tuning

Leverage historical workloads for efficient configuration tuning in complex systems.

Configuration parameter tuning for DBMSs is highly complex due to numerous interdependent knobs, heterogeneous workloads, and dynamic real-world applications.

Modern database management systems (DBMS) characterize workloads via runtime behavior metrics, enhancing performance analysis.

The study focuses on enhancing database configuration tuning using advanced techniques like Bayesian optimization and reinforcement learning.