基于机器学习的数据库系统参数优化方法综述OACSTPCD

Knobs tuning is a key technology that affects the performance and adaptability of databases.However,traditional tuning methods have difficulty in finding the optimal configuration in high-dimensional continuous param-eter spaces.The development of machine learning could bring new opportunities to solve this problem.By summari-zing and analyzing relevant work,existing work was classified according to development time and characteristics,including expert decision-making,static rules,heuristic algorithms,traditional machine learning methods,and deep reinforcement learning methods.The database tuning problem was defined,and the limitations of heuristic al-gorithms in tuning problems were discussed.Traditional machine learning-based tuning methods were introduced,including random forest,support vector machine,decision tree,etc.The general process of using machine learning methods to solve tuning problems was described,and specific implementations were provided.The shortcomings of traditional machine learning models in adaptability and tuning capabilities were also discussed.The principles of deep reinforcement learning models were emphasized,and the mapping relationship between tuning problems and deep reinforcement learning models was defined.Recent relevant work on improving database performance,time consumption and model characteristics was introduced,and the process of building and training agents based on deep neural networks was described.Finally,the characteristics of existing work were summarized,and the re-search hotspots and development directions of machine learning in database tuning were outlined.Distributed sce-narios,multi-granularity tuning,adaptive algorithms and self-maintenance capabilities were identified as future re-search trends.