An efficient swarm intelligence algorithm for Multi-Objective Task Scheduling Optimization in the Context of Cloud Computing

In the modern era cloud computing is a fastest growing field. It provides many facilities to the user to store, manipulate and retrieve the data anytime and anywhere with help of web or internet. Time to completion of tasks and load balancing of virtual machines are two significant issues that need fixing in cloud computing settings. This study suggests a Multi-objective Optimisation technique to overcome these issues. GA, PSO and ACO Algorithm are utilized in the study to schedule tasks with multiple objectives. Makespan, a critical parameter measuring the overall time necessary to accomplish all jobs, is the primary focus of the study since it assesses the performance of various algorithms in great detail. In our research, PSO produces a makespan of 1.1988, GA produces 1.3025, while ACO remarkably achieves a minimum makespan of 0.8736 after 200 iterations. The comparison’s findings demonstrate that ACO is better to PSO and GA, making it the ideal choice for cloud task scheduling algorithm optimization. The significant difference in makespan values demonstrates ACO’s superior capacity to explore the solution space, converge effectively, and offer plans that minimize the overall amount of time required to accomplish the task. The findings of this study are crucial because they demonstrate the advantages of ACO for scheduling several objectives efficiently at once.