An Energy-Efficient Tuning Method for Cloud Servers Combining DVFS and Parameter Optimization
Weiwei Lin, Xiaoxuan Luo, ChunKi Li, Jiechao Liang, Guokai Wu, Keqin Li
Abstract
Emerging cloud computing applications place a growing demand on resources, leading to increasingly large data centers with significant energy consumption and carbon emissions. Various research conduct optimization methods to improve the energy efficiency of the server in the cloud data center. However, most existing optimization methods are designed for specific applications, thus making it difficult to handle complex cloud environments. In this paper, we propose a general parameter optimization method called MPOD to improve the energy efficiency of cloud servers in real time. MPOD considers issues in the cloud environment, such as SLA guarantee, user privacy, and dynamic workloads. We introduce energy efficiency curves to DVFS, implementing a low-overhead, fast response, and general frequency optimization strategy. Moreover, we design a workload classification framework and three prediction models based on machine learning algorithms to achieve accurate and adaptive Linux kernel parameters optimization. According to the experiment, MPOD can improve the energy efficiency of the server by an average of 30.5%, 20.1%, 10.8% in BenchSEE, SERT and TPC-H, respectively.