Vol.27, No.2, 2021, pp.347-358, doi:10.32604/iasc.2021.012401
OPEN ACCESS
ARTICLE
An Enhanced Decentralized Virtual Machine Migration Approach for Energy-Aware Cloud Data Centers
  • R. Jayamala*, A. Valarmathi
University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, 620024, India
* Corresponding Author: R. Jayamala. Email:
Received 30 August 2020; Accepted 13 October 2020; Issue published 18 January 2021
Abstract
Cloud computing is an increasingly important technology to deliver pay-as-you-go online computing services. In this study, the cloud service provider permits the cloud user to pay according to the user’s needs. Various methods have been used to reduce energy utilization in the cloud. The rapid increase of cloud users has led to increased energy consumption and higher operating costs for cloud providers. A key issue in cloud data centers is their massive energy consumption to operate and maintain computing services. Virtual machine (VM) migration is a method to reduce energy consumption. This study proposes enhanced decentralized virtual machine migration (EDVMM), based on a linear prediction model, to decrease energy utilization in cloud data centers, reduce service-level agreement violations with a minimum number of migrated VMs, and enhance resource utilization. The enhanced decentralized approach is used to select the appropriate VMs, and prediction is used to determine the VMs for a host. This EDVM algorithm uses virtualization technology and migrates VMs from overloaded and under-loaded hosts to physical machines (PMs). The work was implemented and evaluated using CloudSim with 10 days of real workload trace provided by PlanetLab.
Keywords
VM consolidation; VM migration; data center; energy efficiency; service level agreement
Cite This Article
R. Jayamala and A. Valarmathi, "An enhanced decentralized virtual machine migration approach for energy-aware cloud data centers," Intelligent Automation & Soft Computing, vol. 27, no.2, pp. 347–358, 2021.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.