基于合作关系的多智能体数据库参数调优模型
Multi-agent Database Parameter Optimization Model Based on Cooperative Relationships
刘钊勇 1张艺婷2
作者信息
- 1. 四川化工职业技术学院数字经济学院,四川泸州 646300;四川师范大学可视化计算与虚拟现实四川省重点实验室,四川成都 610068
- 2. 四川化工职业技术学院基础教学部,四川泸州 646300
- 折叠
摘要
高维数据库参数空间中的参数调优是提高数据库性能的难点,现有方法更多关注于如何识别重要参数,在如何有效提高可调参数数量的问题上仍存在不足.针对上述问题,基于低维度映射技术和多智能体(Multi-Agent)强化学习技术,提出基于合作关系的 Multi-Agent数据库参数调优(Cooperative Multi-Agent Database Parameter Tuning,CMADPT)模型,CMADPT将数据库参数进行分类调优,极大增加了可调参数的数量;提出基于低维度映射的降维模型(Low Dimensional Mapping Model,LDMM),通过低维的合成参数调优高维的数据库参数.实验结果表明,CMADPT模型有效地扩大了可调参数的数量,比主流方法平均提升 1.117%的数据库性能.此外,CMADPT每 300 次迭代训练平均节省 1.32 h,极大地提升了算法的时间性能.
Abstract
Parameter tuning in high-dimensional database parameter space is a difficult problem to improve database performance.Existing methods have drawbacks on how to effectively expand the number of tunable parameters,and these methods focus more on how to identify important parameters.To address above problems,a Multi-Agent-based database parameter tuning model called CMADPT(Cooperative Multi-Agent Database Parameter Tuning)is proposed based on low-dimensional mapping and Multi-Agent reinforcement learning techniques,which classifies database parameters for tuning and greatly increase the number of tunable parameters.A Low Di-mensional Mapping Model(LDMM)is proposed,which tunes high-dimensional database parameters through low-dimensional synthetic parameters.Experimental results show that the CMADPT model can effectively expand the number of tunable parameters and improve the database performance by 1.117%on average when compared with the state-of-the-art methods.In addition,CMADPT can save 1.32 h per 300 iterative training on average,which can greatly improve the runtime performance of the proposed model.
关键词
数据库参数调优/合作关系/深度强化学习/多智能体Key words
database parameter tuning/cooperative relationship/deep reinforcement learning/multi-agent引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据