Improving Virtual Machine Migration Effects in Cloud Computing Environments Using Depth First Inspired Opportunity Exploration

Author:

Kumar Kamal1,Thaman Jyoti2

Affiliation:

1. National Institute of Technology, Uttarakhand, India

2. Independent Researcher, India

Abstract

The cloud platform has established itself as the de-facto standard in IT outsourcing. This is resulting in large-scale migration of infrastructure and development platforms from in-house to cloud service providers. Many recent proposals on cloud platforms have addressed several issues that appeared on the cloud horizon. VM placement (VMP) has been a serious concern when it comes to placement of VMs after migration or VM reallocation. Most of the recent works have lacked multiple VM placement (MVMP) problem instances. A recently researched idea of MVMP through depth first opportunistic exploration (DFOE) is proposed in this paper. The performance of MVMP is compared with existing single VM placement benchmark algorithm. Improvement in terms of number of VM migrations, energy consumption, and VM reallocation is reported through simulation of real-time load scenario. Cloud environments can benefit from MVMP and improve operating margins in terms of power saving and load balancing.

Publisher

IGI Global

Subject

Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction

Reference55 articles.

1. Multiobjective virtual machine placement in cloud environment.;A. C.Adamuthe;International Conference on Cloud & Ubiquitous Computing & Emerging Technologies (CUBE),2013

2. Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers.;M. S.Ajmal;Computers & Electrical Engineering,2021

3. Virtual machine placement optimization supporting performance SLAs.;A.Anand;IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom),2013

4. Efficient VM selection strategies in cloud datacenter using fuzzy soft set.;N.Baskaran;Journal of Organizational and End User Computing,2021

5. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing.;A.Beloglazov;Future Generation Computer Systems,2012

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Mitigating Risks in the Cloud-Based Metaverse Access Control Strategies and Techniques;International Journal of Cloud Applications and Computing;2023-12-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3