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

Abstract

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

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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[1]	WEN Ting-ting, LI Hong-zhe. Elastic Load Balancing Algorithm for Cloud Computing Service [J]. Computer and Modernization, 2019, 0(10): 28-.
[2]	ZHU Ya-hui, CHEN Dan, ZHUANG Yi. Virtual Machine Scheduling Algorithm Based on Energy-Aware in Cloud Data Center [J]. Computer and Modernization, 2016, 0(4): 74-78.

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

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