Load Balancing in Cloud Computing Using Biogeography Based Optimization

Cloud computing depends on sharing of assets to accomplish intelligibility and economies of scale, like a utility over a network. In this various types of job scheduling and load balancing approaches has been used for this purpose. Various tasks on cloud have to be allocated to different resources for prevention of slow computation process at cloud. In the previous work various algorithms and optimization approaches have been used for the purpose of minimizing of make spans. The main purpose of minimization of make span is to compute all the tasks allocated, in less computation time and with less error. The genetic algorithm, Particle swarm optimization approaches has been used for the purpose of optimization of make spans of scheduled tasks. This approach provides not too good results as compare to particle swarm optimization approach. To overcome this limitation a nature based optimization approach BBO (Biography Based Optimization) is used for optimization of make spans developed by various algorithms.