机器学习方法的云数据中心能耗模型研究  

Research on Energy Consumption Model of Cloud Data Center Based on a Machine Learning Method

作　　者：卢洪明 刘先锋 周舟 梁赛 LU Hong-ming;LIU Xian-feng;ZHOU Zhou;LIANG Sai(School of Information Science and Engineering,Hunan Normal University,Changsha 410081,China;School of Computer Engineering and Applied Mathematics,Changsha University,Changsha 410022,China)

机构地区：[1]湖南师范大学信息科学与工程学院,长沙410081 [2]长沙学院计算机工程与应用数学学院,长沙410022

出　　处：《小型微型计算机系统》2023年第9期1966-1973,共8页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(62002115)资助.

摘　　要：随着全球数据中心的大量部署和云计算服务需求激增,其高能耗问题日益严重,如何精确预测数据中心的能耗已成为一项重要研究课题.针对数据中心服务器的能耗具有不确定性和非线性等特点,本文提出了一种机器学习方法的服务器实时能耗预测方法.该方法采用随机森林算法筛选模型的输入参数,使用网格搜索方法优化模型的超参数,利用机器学习方法构建服务器的能耗模型.实验结果表明:与基准算法相比,经过优化后的模型其平均绝对误差降低了6.5%,并且在加入误差置信区间后能耗模型的平均绝对误差低于1.4%.With the massive deployment of data centers around the world and the surging demand for cloud computing services,the problem of high energy consumption is becoming more and more serious.How to accurately predict the energy consumption of data centers has become an important research topic.In view of the uncertainty and nonlinear characteristics of server energy consumption in the data centers,a real-time server energy consumption prediction method based on machine learning is proposed in this paper.The random forest algorithm is used to filter the input parameters of the model.The grid search method is leveraged to optimize the hyper-parameters of the model,and the machine learning method is used to build the server power model.Experimental results show that compared with the benchmark algorithm,the average absolute error of the optimized model is reduced by 6.5%,and the average absolute error of the energy consumption model is less than 1.4%after adding the error confidence interval.

关 键 词：数据中心 云计算 能耗预测 随机森林 超参数 

分 类 号：TP316[自动化与计算机技术—计算机软件与理论]