A Study on Nature Inspired Algorithms for Load Balancing in Cloud Computing

In a cloud computing (CC) setting, tasks are distributed across virtual machines (VMs) with varying lengths, start times, and execution times. Load balancing(LB) must be performed in such a way that all VMs are balanced in order to obtain optimal usage of their capabilities and increase system performance. The objective of the study is to explore and examine the Load balancing(LB) technique using natureinspired algorithms to optimally schedule all incoming tasks to the available VMs in order to reduce makespan (MS) and enhance machine usage in cloud computing. The explored nature inspired algorithms in this study includes Honey Bee Load Balancing(HLB), Particle Swarm Optimization(PSO), Rock Hyrax Optimization(RHO), Ant Colony Optimization(ACO), Birds Swarm Optimization (BSO), Mayfly Optimization (MFO), Crow Search Algorithm(CSA), Grey Wolf Algorithm(GWO), Lion Optimization Algorithm(LOA), Harris Hawks optimization (HHO) for LB in cloud computing. The scheduling technique is implemented using the CloudSim simulator. The simulation results clearly show that the nature-inspired techniques scheduling algorithms performance is found to be efficient in terms of lowering MS and energy consumption (EC).