中圖分類號(hào)： TP393.4 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211692 中文引用格式： 徐勝超,，宋娟,，潘歡. 云數(shù)據(jù)中心基于皮爾遜相關(guān)系數(shù)的虛擬機(jī)選擇策略[J].電子技術(shù)應(yīng)用，2021,，47(10)：77-81. 英文引用格式： Xu Shengchao,，Song Juan，Pan Huan. The Pearson correlation coefficient based virtual machine selection strategy for cloud[J]. Application of Electronic Technique,，2021,，47(10)：77-81.
The Pearson correlation coefficient based virtual machine selection strategy for cloud
Xu Shengchao1，Song Juan2,，Pan Huan2
1.School of Date Science,，Guangzhou HuaShang College，Guangzhou 511300,，China,； 2.Ningxia Key Lab of Intelligent Sensing for Desert Information，Ningxia University,，Yinchuan 750021,，China
Abstract： 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.
Key words : Pearson correlation coefficient；virtual machine selection,；energy consumption model,；cloud data centers,；virtual machine migration
