Response time analysis of dynamic load balancing algorithms in Cloud Computing

Due to rapid and tremendous growth in cloud computing, there is a huge demand by the applications for faster services hosted in the cloud. Minimizing the latency in the cloud requires approaches that can handle the requests of clients. Load balancing is the mechanism to distribute tasks to computational entities in a cloud computing environment to manage current needs of the application. There is need for efficient load balancing mechanisms to improve the overall performance of cloud computing environment by reducing the time to respond to the request. In this paper three algorithms to balance the load for cloud computing are compared with respect to minimization of job response time. Equally spread current execution, throttled and particle swarm optimization, a Swarm Intelligence algorithm are the algorithms considered for load balancing. The algorithms are simulated using CloudSim simulator. Performance measurement of these algorithms show that the particle swarm optimization balances the workload with reduced job response time as compared with the other two.