A hybrid GWO-PSO Algorithm for Load Balancing in Cloud Computing Environment

Cloud computing dynamically allocates virtual resources as per the demands of users. The rapid increase of data computation and storage in cloud computing environment results in uneven distribution of workload on its heterogeneous resources. As a result of that, overloaded servers will have a higher job completion time compared to the corresponding time taken by under loaded servers in the same environment. Distributing balanced workload over the available resources is a key challenge in cloud computing environment. Traditionally, load balancing is used to distribute the workload among multiple servers and to avoid overloading and under loading of servers. It also helps to improve system performance and fair utilization of resources. In this paper, we present a novel hybrid load balancing approach in cloud computing environment using Grey Wolf Optimization based Particle Swarm Optimization and compare it with Harmony Search, Artificial Bee Colony, Particle Swarm Optimization and Grey Wolf Optimization algorithms. It also helps to improve system performance and fair utilization of resources. Results of research experiments are very encouraging with improved convergence and simplicity.