Abstract: In cloud databases,there are numerous configuration options,including internal database parameters and virtual machine resource configuration for the environment deployment,which collectively determine the database’s read/write performance and resource consumption.In the cloud environment with elastic resources,users are concerned about both the database’s service performance and resource consumption costs.However,due to the large number of configuration options and rapid workload changes,finding the optimal combination of configurations becomes challenging.To address the online tuning scenario with dynamically changing workloads,this paper proposes CoTune,a fast tuning method for coordinating cloud database resources and parameters.This method focuses on OLTP workloads and iteratively adjusts the configurations of virtual machine resources and database parameters to minimize resource consumption while ensuring service quality.The method introduces several key innovations:firstly,it adopts a three-stage approach within each tuning cycle to adjust resource quotas and database parameters,prioritizing service quality;secondly,it classifies the impact of database parameters on different resources,reducing the search space and enabling rapid parameter adjustments;and finally,it incorporates a reinforcement learning model for database parameter tuning,with a specific reward function designed to quickly obtain reward values and accelerate the tuning frequency.Experimental results demonstrate that,compared to approaches that simultaneously tune resources and parameters or solely focus on resource tuning,the proposed method reduces resource consumption while maintaining service quality.Through rapid iterative tuning,it effectively addresses the challenges posed by workload variations and achieves more efficient resource utilization in dynamic workload environments.

Key words: Cloud database, Parameter tuning, Quality-of-Service, Resource consumption