摘要
提出了云数据中心基于皮尔逊相关系数的虚拟机选择策略(Pearson Correlation coefficient Virtual Machine Selection,PC-VMS)。PC-VMS把统计学中的皮尔逊相关系数应用于虚拟机CPU历史利用率数据,建立了衡量每对虚拟机CPU利用率之间的相关性的数学模型;PC-VMS会获取每对虚拟机最近n次的CPU利用率,根据输入的两组数据来计算皮尔逊相关系数,最后在一组相关性最高的虚拟机中选择一个CPU利用率最高的进行迁移,随后结合虚拟机放置策略分配到新的目标物理主机上。仿真结果表明,PC-VMS与CloudSim4.0内置的虚拟机选择策略相比,各类性能指标都有改善,PC-VMS可以为企业节能云数据中心的构造提供参考。
A Pearson correlation coefficient virtual machine selection approach called PC-VMS was proposed and discussed in this paper.PC-VMS uses the calculation method in statistics of Pearson correlation coefficient for historical CPU utilization data of virtual machines,and establishes a measurement of the CPU utilization of each pair of virtual machines.The mathematical model of the correlation between the rates was also constructed.The PC-VMS algorithm will obtain the CPU utilization of the last n times for each pair of virtual machines,calculate the Pearson correlation coefficient based on the two sets of input data,and finally select the virtual machines in the group of the highest correlation and allocate it on the target physical host.The experimental results and performance analysis show this strategy leads to a further improvement compared with the old migration strategies in CloudSim4.0.This strategy is valuable for other cloud providers to build a low energy consumption cloud data center.
作者
徐胜超
宋娟
潘欢
Xu Shengchao;Song Juan;Pan Huan(School of Date Science,Guangzhou HuaShang College,Guangzhou 511300,China;Ningxia Key Lab of Intelligent Sensing for Desert Information,Ningxia University,Yinchuan 750021,China)
出处
《电子技术应用》
2021年第10期77-81,共5页
Application of Electronic Technique
基金
广州华商学院校内导师制科研项目(2020HSDS04,2021HSDS15)
国家自然科学基金项目(青年基金)(61403219)。
关键词
皮尔逊相关系数
虚拟机选择
能量消耗模型
云数据中心
虚拟机迁移
Pearson correlation coefficient
virtual machine selection
energy consumption model
cloud data centers
virtual machine migration
