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.

参考文献［24］

［1］	LEI Z, SUN E, CHEN S B, et al. A novel hybrid-copy algorithm for live migration of virtual machine ［J］. Future Internet, 2017,9（3）：1-13.
［2］	SHI T, MA H, CHEN G. Energy-aware container consolidation based on PSO in cloud data centers［C］// 2018 IEEE Congress on Evolutionary Computation. 2018:1-8.
［3］	BELOGLAZOV A, BUYYA R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers［J］. Concurrency & Computation: Practice & Experience, 2012,24（13）:1397-1420.
［4］	SHAW S B, SINGH A K. Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center［J］. Computers & Electrical Engineering, 2015,47:241-254.
［5］	ZHOU Z, HU Z G, LI K Q. Virtual machine placement algorithm for both energy-awareness and SLA violation reduction in cloud data centers［J］. Scientific Programming, 2016, 2016:5612039:1-5612039:11.
［6］	MANN Z A. Interplay of virtual machine selection and virtual machine placement［C］// European Conference on Service-oriented & Cloud Computing. 2016:137-151.
［7］	JOSEPH C T, MARTIN J P. Task dependency aware selection （TDAS） in cloud ［J］. Procedia Computer Science, 2016,93:269-275.
［8］	KAAOUACHE M A, BOUAMAMA S. Solving bin packing problem with a hybrid genetic algorithm for VM placement in cloud［J］. Procedia Computer Science, 2015,60（1）:1061-1069.
［9］	ALBOANEEN D A, TIANFIELD H, Zhang Y. Glowworm swarm optimisation algorithm for virtual machine placement in cloud computing［C］// 2016 IEEE International Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress. 2016:808-814.
［10］	VASUDEVAN M, TIAN Y C, TANG M L, et al. Energy-efficient application assignment in profile-based data center management through a repairing genetic algorithm［J］. Applied Soft Computing, 2018,67（3）:399-408.
［11］	DUGGAN M, FLESK K, DUGGAN J, et al. A reinforcement learning approach for dynamic selection of virtual machines in cloud data centres［C］// The 6th IEEE International Conference on Innovative Computing Technology. 2016:273-286.
［12］	USMAN M J, ISMAIL A S, CHIZARI H, et al. Energy-efficient virtual machine allocation technique using flower pollination algorithm in cloud data center: A panacea to green computing［J］. Journal of Bionic Engineering, 2019,16（2）:354-366.
［13］	FARAHNAKIAN F, ASHRAF A, PAHIKKALA T, et al. Using ant colony system to consolidate VMs for green cloud computing［J］. IEEE Transactions on Services Computing, 2015,8（2）:187-198.
［14］	MAURYA K, SINHA R. Energy conscious dynamic provisioning of virtual machines using adaptive migration thresholds in cloud data center［J］. International Journal of Computer Science and Mobile Computing, 2013,2（3）:74-82.
［15］	MHEDHEB Y, JRAD F, JIE T, et al. Load and Thermal-aware VM Scheduling on the Cloud［M］. Springer-Verlag New York Inc., 2013.
［16］	KHOSHKHOLGHI M A, DERAHMAN M N, ABDULLAH A, et al. Energy-efficient algorithms for dynamic virtual machine consolidation in cloud data centers［J］. IEEE Access, 2017（5）:10709-10722.
［17］	WANG J V, CHENG C T, TSE C K. A power and thermal-aware virtual machine allocation mechanism for cloud data centers［C］// IEEE International Conference on Communication Workshop. 2015:2850-2855.
［18］	王斌,王勤为,董科,等. 基于二次指数平滑预测的虚拟机调度方法研究［J］. 计算机应用研究, 2017,34（3）:723-726.
［19］	WANG J V, FOK K Y, CHENG C T, et al. A stable matching-based virtual machine allocation mechanism for cloud data centers［C］// 2016 IEEE World Congress on Services. 2016:103-106.
［20］	FARAHNAKIAN F, PAHIKKALA T, LILJEBERG P, et al. Energy aware consolidation algorithm based on K-nearest neighbor regression for cloud data centers［C］// IEEE/ACM International Conference on Utility & Cloud Computing. 2013:256-259.
［21］	GUERARD J B. Introduction to Financial Forecasting in Investment Analysis［M］. Springer Science and Business Media, New York, 2013.
［22］	WEISBERG S. Applied Linear Regression［M］. John Wiley & Sons Inc., 2005.
［23］	SPEC. Benchmarks, Standard Performance Evaluation Corporation［S/OL］.［2020-09-03］. https://www.spec.org/.
［24］	WANG J V, CHENG C T, TSE C K. Effects of correlation-based VM allocation criteria to cloud data centers［C］// IEEE International Conference on Cyber-enabled Distributed Computing & Knowledge Discovery. 2016:398-401.

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