Title: A dynamic model for energy-efficiently migrating a group of virtual machines from a host server to a guest server

Authors: Dilawaer Duolikun; Tomoya Enokido; Makoto Takizawa

Addresses: Department of Advanced Sciences, Hosei University, Tokyo, Japan ' Faculty of Business Administration, Rissho University, Tokyo, Japan ' Department of Advanced Sciences, Hosei University, Tokyo, Japan

Abstract: In this paper, we newly propose a dynamic migration of a group of υ virtual machines [DMG(υ)] algorithm to reduce the total electric energy consumption of servers in a cluster. Here, virtual machines are dynamically resumed and suspended so that the number of processes on each virtual machine can be kept fewer. In addition, a group of υ (υ ≥ 1) virtual machines migrate from a host server to a guest server so that the total electric energy consumed by the servers can be reduced. In our previous studies, time to migrate each virtual machine is assumed to be zero. In reality, there is migration time every process is suspended on a virtual machine migrating even in the live manner. Hence, the more often a virtual machine migrates, the longer time it takes to perform processes on the virtual machine. We propose a model to estimate electric energy to be consumed by servers through considering the migration time of each virtual machine. By using the estimation model, we can decide on which virtual machines on a host server to which guest server so that the total electric energy consumption of the host and guest servers can be reduced. In the evaluation, we show the total electric energy consumption of servers can be reduced by the DMG(υ) algorithm compared with other algorithms.

Keywords: energy-efficient computation; power consumption model; dynamic virtual machine migration; group migration of virtual machines; DMG(υ) algorithm.

DOI: 10.1504/IJSSC.2018.097287

International Journal of Space-Based and Situated Computing, 2018 Vol.8 No.3, pp.138 - 150

Received: 07 Aug 2018
Accepted: 24 Aug 2018
Published online: 10 Jan 2019 *

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