This paper provides a comprehensive overview of predominant methodologies utilized in the automatic tuning of parameters within database management systems, examining techniques like Bayesian optimization and Reinforcement learning.
The research breaks down the tuning process into components such as tuning objectives, workload characterization, feature pruning, and configuration recommendation, offering insights into the strategic intricacies of each phase.
Existing tuning methodologies in this study emphasize the importance of workload characterization to adapt to the dynamic requirements of on-demand cloud applications and their performance optimizations.
By systematically dissecting the tuning process, we can better understand how to approach parameter optimization, taking into account overhead, safety concerns, and adaptivity in DBMS performance.
#database-management-systems #automatic-tuning #configuration-parameter-optimization #machine-learning #cloud-applications
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