一种基于机器学习的虚拟机放置方法  

A Method for Virtual Machine Placement Based on Machine Learning

作　　者：郭良敏 高俊杰[1,2] 胡桂银 GUO Liangmin;GAO Junjie;HU Guiyin(School of Computer and Information,Anhui Normal University,Wuhu,Anhui 241003,China;Anhui Provincial Key Laboratory of Network andInformation Security,Anhui Normal University,Wuhu,Anhui 241003,China)

机构地区：[1]安徽师范大学计算机与信息学院,安徽芜湖241003 [2]安徽师范大学网络与信息安全安徽省重点实验室,安徽芜湖241003

出　　处：《计算机工程》2019年第5期135-142,共8页Computer Engineering

基　　金：安徽省自然科学基金(1908085MF190;1508085QF133;1808085MF172)

摘　　要：为改善云数据中心的能耗、负载均衡性和服务等级协议(SLA)违背率,对虚拟机放置策略进行优化。基于IaaS环境,提出一种基于机器学习的虚拟机迁移调整方法。根据资源消耗的互补性和不均衡性对虚拟机进行预放置,使用深度神经网络预测物理机负载等级,并利用深度Q网络调整物理机数量。实验结果表明,该方法能够有效均衡负载分布,降低能源开销和SLA违背率。In order to improve energy consumption,load balance,and Service Level Agreement(SLA) violation rate of cloud data centers,it is necessary to optimize virtual machine placement strategy.Therefore,based on the IaaS environment,a virtual machine migration adjustment method based on machine learning is proposed.The virtual machine is pre-placed according to the complementarity and imbalance of resource consumption,the deep neural network is used to predict the physical machine load level,and the Deep Q Network(DQN) is used to adjust the number of physical machines.Experimental results show that this method can effectively balance load distribution,reduce energy cost and SLA violation rate.

关 键 词：虚拟机放置 机器学习 能耗 负载均衡 服务等级协议 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]