Open Access

ARTICLE

Multi-Objective Optimization of Energy Aware Virtual Machine Placement in Cloud Data Center

B. Gomathi¹, B. Saravana Balaji², V. Krishna Kumar³, Mohamed Abouhawwash^4,5,*, Sultan Aljahdali⁶, Mehedi Masud⁶, Nina Kuchuk⁷

1 Department of Information Technology, Hindusthan College of Engineering and Technology, Coimbatore, 641032, India
2 Department of Information Technology, Lebanese French University, Erbil, 44001, Iraq
3 Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, 641022, India
4 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
5 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA
6 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
7 Department of Electronic Computing, Kharkiv Polytechnic Institute, National Technical University, Kharkiv, 61002, Ukraine

* Corresponding Author: Mohamed Abouhawwash. Email:

Intelligent Automation & Soft Computing 2022, 33(3), 1771-1785. https://doi.org/10.32604/iasc.2022.024052

Received 01 October 2021; Accepted 10 December 2021; Issue published 24 March 2022

Abstract

Cloud computing enables cloud providers to outsource their Information Technology (IT) services from data centers in a pay-as-you-go model. However, Cloud infrastructure comprises virtualized physical resources that consume huge amount of energy and emits carbon footprints to environment. Hence, there should be focus on optimal assignment of Virtual Machines (VM) to Physical Machines (PM) to ensure the energy efficiency and service level performance. In this paper, The Pareto based Multi-Objective Particle Swarm Optimization with Composite Mutation (PSOCM) technique has been proposed to improve the energy efficiency and minimize the Service Level Agreement (SLA) violation in Cloud Environment. In this paper, idea of MOPSO is extended with three distinct features such as Largest Processing Time (LPT) rule is applied to improve load balancing across the resources which leads to energy saving in Cloud Environment; Epsilon Fuzzy Dominance technique is used to select solutions near to Pareto front which improves the diversity of Pareto optimal solutions; and Discrete PSO along with Composite Mutation strategy in the proposed algorithm help to provide better convergence than existing approaches. Hence, the proposed algorithm produced better results than other existing algorithm such as GA and heuristics-based approach.

---

Keywords

---