Virtual machine optimization to achieve energy efficient optimum resource utilization in cloud data center

Muhammad Aamir Panhwar; Sijjad Ali Khuhro; Tehseen Mazhar; Deng Zhong Liang; Mughal E Azam Bilal; Nabeel Qadir

doi:10.17485/IJST/v13i13.211

Article

Virtual machine optimization to achieve energy efficient optimum resource utilization in cloud data center

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i13.211

Year: 2020, Volume: 13, Issue: 13, Pages: 1423-1434

Original Article

Virtual machine optimization to achieve energy efficient optimum resource utilization in cloud data center

Muhammad Aamir Panhwar^1*, Sijjad Ali Khuhro², Tehseen Mazhar³, Deng Zhong Liang¹, Mughal-E-Azam Bilal³, Nabeel Qadir³

1School of Electronic Engineering, Beijing University of Posts and Telecommunications, China
²School of Computer Science and Technology, University of Science and Technology of China, China
³School of Computer Science, Virtual University of Pakistan, Pakistan

*Corresponding author
Muhammad Aamir Panhwar
School of Electronic Engineering, Beijing University of Posts and Telecommunications, China Email: [email protected]

Received Date:09 April 2020, Accepted Date:23 April 2020, Published Date:25 May 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: Cloud offers multiple benefits through its data center-based services. The whole world uses these services that are hosted by physical machines. Millions of virtual machines get an optimized utilization of hardware to utilize these physical machines. The unbalanced distribution of virtual machines to physical machines offers an in-efficient utilization of data center hardware that will also lead to more carbon emission and harm the environment as well. Methods/material: A learning function is needed to offer energy efficiency in the cloud data center through VM optimization through its optimal allocation to physical machines. Therefore, an optimal VM placement and migration algorithm is a challenge that is addressed in this paper to reach an efficient energy optimization and resource utilization level. Finding/Novelty: The proposed algorithm is led by a learning function that takes into account the available number of physical machines, number of virtual machines, incoming requests and decides to run an optimal number of physical machines to obtain energy efficiency level for the cloud data center by migrating the virtual machines (VMs).

Keywords: Datacenter; Cloud computing; Energy efficiency; VM placement; VM migration; Green Cloud

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Copyright

© 2020 Panhwar, Khuhro, Mazhar, Liang, Bilal, Qadir. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)