Open Access

ARTICLE

A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

by Iftikhar Ahmad^1,*, Ambreen Shahnaz², Muhammad Asfand-e-Yar³, Wajeeha Khalil¹, Yasmin Bano¹

1 Department of Computer Science and Information Technology, University of Engineering and Technology, Peshawar, 25120, Pakistan
2 Department of Computer Science, Shaheed Benazir Bhutto Women University, Peshawar, 25000, Pakistan
3 Center of Excellence in Artificial Intelligence, Department of Computer Science, Bahria University, Islamabad, Pakistan

* Corresponding Author: Iftikhar Ahmad. Email:

Computers, Materials & Continua 2023, 74(1), 279-291. https://doi.org/10.32604/cmc.2023.031344

Received 15 April 2022; Accepted 08 June 2022; Issue published 22 September 2022

Abstract

The demand for cloud computing has increased manifold in the recent past. More specifically, on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs. The cloud service provider fulfills different user requirements using virtualization - where a single physical machine can host multiple Virtual Machines. Each virtual machine potentially represents a different user environment such as operating system, programming environment, and applications. However, these cloud services use a large amount of electrical energy and produce greenhouse gases. To reduce the electricity cost and greenhouse gases, energy efficient algorithms must be designed. One specific area where energy efficient algorithms are required is virtual machine consolidation. With virtual machine consolidation, the objective is to utilize the minimum possible number of hosts to accommodate the required virtual machines, keeping in mind the service level agreement requirements. This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host. The online algorithm is analyzed using a competitive analysis approach. In addition, an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms. Our proposed online algorithm consumed 25% less energy and performed 43% fewer migrations than the benchmark algorithms.

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Keywords

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