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

B. Gomathi1, B. Saravana Balaji2, V. Krishna Kumar3, Mohamed Abouhawwash4,5,*, 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: email

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

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.

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APA Style
Gomathi, B., Balaji, B.S., Kumar, V.K., Abouhawwash, M., Aljahdali, S. et al. (2022). Multi-objective optimization of energy aware virtual machine placement in cloud data center. Intelligent Automation & Soft Computing, 33(3), 1771-1785. https://doi.org/10.32604/iasc.2022.024052
Vancouver Style
Gomathi B, Balaji BS, Kumar VK, Abouhawwash M, Aljahdali S, Masud M, et al. Multi-objective optimization of energy aware virtual machine placement in cloud data center. Intell Automat Soft Comput . 2022;33(3):1771-1785 https://doi.org/10.32604/iasc.2022.024052
IEEE Style
B. Gomathi et al., "Multi-Objective Optimization of Energy Aware Virtual Machine Placement in Cloud Data Center," Intell. Automat. Soft Comput. , vol. 33, no. 3, pp. 1771-1785. 2022. https://doi.org/10.32604/iasc.2022.024052



cc 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.
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