Optimizing Cloud Load Balancing: A Nature-Inspired Approach for Efficient Task Scheduling and Resource Optimization in Scalable Cloud Computing Environments

Cloud load balancing is a key part of making sure that tasks are scheduled and resources are used in the best way possible in scalable cloud computing settings. This study suggests a way to improve load balancing that is based on nature and uses methods as Particle Swarm Optimization (PSO) and NIVM PSO Optimized (NIVM-PSO). By acting like natural processes, these programs can adapt to changing workloads and make sure that tasks are spread out evenly across computers. The goal of the suggested model is to cut down on reaction time, make the best use of resources, and boost system performance as a whole. The results of the experiments show that when compared to standard load balancing methods, they are much better at spreading out the load, lowering delay, and increasing speed. This nature-inspired method is a strong way to handle the complexity and demands of modern cloud systems. It creates a framework that can grow and work well for future cloud computing apps.