计算机工程 ›› 2019, Vol. 45 ›› Issue (5): 135-142.doi: 10.19678/j.issn.1000-3428.0052354

所属专题: 机器学习

• 人工智能及识别技术 • 上一篇    下一篇

一种基于机器学习的虚拟机放置方法

郭良敏a,b,高俊杰a,b,胡桂银a,b   

  1. 安徽师范大学 a.计算机与信息学院; b.网络与信息安全安徽省重点实验室,安徽 芜湖 241003
  • 收稿日期:2018-08-10 出版日期:2019-05-15 发布日期:2019-05-15
  • 作者简介:郭良敏(1980—),女,副教授、博士,主研方向为云计算、服务推荐、信息安全;高俊杰,学士;胡桂银,讲师、硕士。
  • 基金项目:

    安徽省自然科学基金(1908085MF190,1508085QF133,1808085MF172)。

A Method for Virtual Machine Placement Based on Machine Learning

GUO Liangmina,b,GAO Junjiea,b,HU Guiyina,b   

  1. a.School of Computer and Information; b.Anhui Provincial Key Laboratory of Network and Information Security,Anhui Normal University,Wuhu,Anhui 241003,China
  • Received:2018-08-10 Online:2019-05-15 Published:2019-05-15

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。

关键词: 虚拟机放置, 机器学习, 能耗, 负载均衡, 服务等级协议

Abstract:

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.

Key words: virtual machine placement, machine learning, energy consumption, load balance, Service Level Agreement(SLA)

中图分类号: