Bayesian Optimization is pivotal in tuning DBMS parameters, incorporating surrogate models to effectively minimize or maximize an objective function through adaptive sampling, improving overall DBMS performance.
The exploration of automatic parameter recommendation methodologies categorizes them into Bayesian Optimization, Reinforcement Learning, Neural Network Solutions, and Search-based Solutions, each addressing distinct DBMS challenges.
By utilizing surrogate models and acquisition functions, Bayesian Optimization enhances search efficiency in configuration tuning, ensuring optimized performance in Database Management Systems, essential for complex workloads.
The paper delineates the strengths and weaknesses of each tuning methodology, emphasizing their tailored approaches for various scenarios in database management, thus facilitating more effective configuration recommendations.
#database-management-systems #bayesian-optimization #parameter-tuning #reinforcement-learning #machine-learning
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