Two-Stage Optimal Virtual Machine Load Balancing Algorithm for Cloud Computing

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : E. Suganthi, F. Kurus Malai Selvi

10.14445/23488549/IJECE-V12I3P118

How to Cite?

E. Suganthi, F. Kurus Malai Selvi, "Two-Stage Optimal Virtual Machine Load Balancing Algorithm for Cloud Computing," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 3, pp. 179-189, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I3P118

Abstract:

Cloud Computing (CC) is allocating resources flexibly to deliver services to end users via the Internet. To implement CC, it is necessary to tackle various obstacles, including resource finding, security, scheduling, and Load Balancing (LB). LB is the most difficult of these research problems. LB aims to allocate workloads to optimize resource usage and boost performance. This research paper proposes an efficient LB model for CC using a two-stage optimal meta-heuristic algorithm called TSOVM_LB. In the first stage, the Virtual Machine (VM) is chosen based on the Minimum Utilization and Migration (MUM) time. In the second stage, a multi-objective optimization algorithm, Modified Fish Swarm Optimization (MFSO), is used for VM allocation. This model allows the VM to the Physical Machine (PM). The proposed method was assessed using CloudSim, incorporating massive VMs and workload traces from the PlanetLab platform. The outcomes showed that the proposed technique attained much higher levels of energy efficiency, SLA compliance, and fewer VM migrations related to other modern techniques. The results presented here provide evidence of the efficacy of the proposed technique in optimizing the allocation of VMs in a cloud environment.

Keywords:

Load Balancing, Optimization, Fish Swarm, Cloud Computing, Virtual Machine.

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