Hybrid Load Balancing Technique for Cloud Environment Using Swarm Optimization

One of the most challenging aspects of cloud computing is task scheduling. User needs are changing rapidly in a dynamic environment, and the resources can fluctuate depending on demand because they are virtual. This study presents a hybrid task scheduling model that combines Particle Swarm Optimization and Whale Optimization techniques to address the challenges of task scheduling and achieve the best performance. The method is analyzed and scored based on its "makespan," "resource utilization," and "convergence." Test results indicate that the proposed method reduces the makespan in all cases. Additionally, it increases resource utilization compared to the existing state-of-the-art methods. Furthermore, resource utilization increases across the board with the number of tasks performed.