Department of Computer Science, Himachal Pradesh University, Shimla-171005, Himachal Pradesh, India
Email: ajayprashar93@gmail.com (A.P.); jawahar.hpu@gmail.com (J.T.)
*Corresponding author
Manuscript received September 20, 2023; revised October 19, 2023; accepted February 7, 2024; published July 11, 2024
Abstract—Cloud computing is popular among industries, academia, and government to supply reliable and scalable computational power. High-speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the cloud service providers to manage resources effectively but unoptimized and inefficient services degrade the performance of the system. The scheduling architecture of cloud computing includes Physical Machines (PMs), Virtual Machines (VMs) and the allocation and migration policy of the VMs over the PMs. The overutilized PMs get few migrations and this paper introduces novel behavior of VM selection from overutilized PMs using Swarm intelligence. The evaluation of the proposed algorithm architecture is compared with another state-of-the-art optimization algorithm from the same series. The evaluation has been done on the base of Quality of Service (QoS) parameters, such as Service Level Agreement (SLA) violation, and energy consumption against various load variation scenarios to support elasticity. The proposed algorithm has outperformed other techniques by considerable margin in terms of QoS, and the details are presented in the results section. The simulation results demonstrate that the proposed technique exhibits 6.3% and 6.7% enhancement in terms of reduced energy consumption compared to both Cuckoo Search (CS) and general Dragonfly (DF) techniques, and 3% decrease in SLA violations in comparison to current methods. Additionally, the results reveal an 11% enhancement in VM migration compared to existing approaches.
Keywords—cloud computing, Virtual Machine (VM) placement, migration, dragonfly, Cuckoo Search (CS)
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Cite: Ajay Prashar and Jawahar Thakur, "An Energy-Efficient VM Selection Using Updated Dragonfly Algorithm in Cloud Computing," International Journal of Computer Theory and Engineering, vol. 16, no. 3, pp. 76-86, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
