Enhancing Cloud Load Balancing with Multi-Objective Optimization in Task Scheduling

doi:10.23940/ijpe.25.05.p5.278287

Abstract

Abstract: In cloud computing, efficient workload management is essential for improving resource utilization, service availability, and reliability. While extensive research exists on cloud task scheduling, there remains a gap in addressing multi-objective optimization and load balancing. This study introduces a hybrid optimization method for cloud scheduling aimed at optimizing resource use and improving user service. Specifically, a combined ABC and PSO approach is employed for load balancing optimization. To further enhance the performance of this metaheuristic optimization framework, the SJF heuristic is utilized to generate the initial population. Simulations are conducted using the CloudSim cloud simulator. The primary goals are minimizing makespan and cost while maximizing cloud resource utilization. Performance is evaluated by comparing the proposed work with existing techniques like ABC, PSO, GA, and ACO, using turnaround, waiting time, makespan, throughput, cost, and resource utilization to demonstrate the effectiveness of the proposed work.

Key words: heuristic, meta-heuristic, optimization, load balancing, hybridization, cloud computing

Suman Lata, Dheerendra Singh, and Gaurav Raj. Enhancing Cloud Load Balancing with Multi-Objective Optimization in Task Scheduling [J]. Int J Performability Eng, 2025, 21(5): 278-287.

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