Multi-agent Database Parameter Optimization Model Based on Cooperative Relationships
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.
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