基于线性回归与最小二乘法的物理主机状态异常检测方法

计算机与现代化 ›› 2021, Vol. 0 ›› Issue (05): 105-111.

基于线性回归与最小二乘法的物理主机状态异常检测方法

（1．广州华商学院数据科学学院,广东广州511300；
2．宁夏大学宁夏沙漠信息智能感知重点实验室，宁夏银川750021）

出版日期:2021-06-03 发布日期:2021-06-03
作者简介:徐胜超(1980—)，男，湖北武汉人，讲师,硕士，研究方向：并行分布式处理软件，E-mail: isdooropen@126.com; 宋娟(1980—)，女，河南虞城人，副教授，硕士，研究方向：智能计算，数据挖掘；潘欢(1983—)，男，甘肃靖远人，副教授，博士，研究方向：智能体协调控制，系统优化分析。
基金资助:
国家自然科学基金资助项目(青年基金) (61403219); 广州华商学院校内导师制科研项目资助(2020HSDS04)

A Detection Approach of Physical Host Status Anomalousness Based on Linear Regression and Least Squares

（1.School of Data Science, Guangzhou Huashang College, Guangzhou 511300, China;
2.Ningxia Key Lab of Intelligent Sensing for Desert Information, Ningxia University, Yinchuan 750021, China）

Online:2021-06-03 Published:2021-06-03

摘要/Abstract

摘要： 提出一种基于线性回归与最小二乘法的物理主机状态异常检测方法EPADA（Efficient Physical host status Anomalous Detection Approach）。EPADA可以预测出所有物理主机在将来一段时间内的资源使用率情况，在实体迁移过程中被用来判断物理主机是超负载或低负载。当某个主机超负载时，则将一些虚拟机迁移到其他主机上以减少SLA违规率。当某个主机低负载时，该主机切换到睡眠状态以减少能量消耗。EPADA物理主机状态异常检测方法通过CloudSim来实现和仿真,仿真结果表明了EPADA的良好性能。

关键词: 超负载状态检测, 低负载状态检测, 虚拟机迁移, 处理器使用率, 云数据中心

Abstract: A detection approach of physical host status anomalousness based on linear regression and least squares called EPADA （Efficient Physical host status Anomalousness Detection Approach） is proposed. EPADA can predict the CPU utilization for a period of time in the future based on the history of usage in each host. It is used in the live migration process to predict over-loaded and under-loaded hosts. When a host becomes over-loaded, some virtual machines migrate to other hosts to reduce SLA violation. When a host becomes under-loaded, the host switches to the sleep mode for reducing power consumption. EPADA is implemented and simulated by CloudSim. Simulation results show the good performance of EPADA.

Key words: over-loaded host status detection, under-loaded host status detection, virtual machine migration, CPU utilization, cloud data centers

徐胜超, 宋娟, 潘欢. 基于线性回归与最小二乘法的物理主机状态异常检测方法[J]. 计算机与现代化, 2021, 0(05): 105-111.

XU Sheng-chao, SONG Juan, PAN Huan. A Detection Approach of Physical Host Status Anomalousness Based on Linear Regression and Least Squares[J]. Computer and Modernization, 2021, 0(05): 105-111.

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