Cloud Heterogeneous Networks: Cooperative Random Spatial Local Best Particle Swarm Optimization for Load Balancing

Cloud services are growing in popularity and undergoing substantial change. To maximize performance, it is necessary to distribute the workload efficiently across multiple virtual machines (VMs). Therefore, a new cooperative LB method called Random Spatial Local Best Particle Swarm Optimization (RSLbestPSO) in cloud computing heterogeneous networks is developed to balance the workload on all VMs efficiently. Unlike traditional approaches, RSLbestPSO aims to increase performance by decreasing response time, finding the most efficient VMs, and improving the response time. The RSLbestPSO works by initializing the particles of which the fitness function will be computed, and the solution with the highest fitness is considered the best solution. The experiments showed that the proposed work effectively balanced the load on the VMs by finding the optimal solution, reducing the makespan time, and increasing the response time. The evaluated results show the effectiveness of the proposed RSLbestPSO.