Cluster Based Load Balancing in Cloud Computing

For a cloud datacenter the biggest issue is how to tackle billions of requests coming dynamically from the end users. To handle such requests efficiently and effectively, there is a need to distribute the load evenly among the cloud nodes. To achieve this goal, various load balancing approaches have been proposed in the past years. Load balancing strategies aim at achieving high user satisfaction by minimizing response time of the tasks and improving resource utilization through even and fair allocation of cloud resources. The traditional Throttled load balancing algorithm is a good approach for load balancing in cloud computing as it distributes the incoming jobs evenly among the VMs. But the major drawback is that this algorithm works well for environments with homogeneous VMS, does not considers the resource specific demands of the tasks and has additional overhead of scanning the entire list of VMs every time a task comes. In this paper, these issues have been addressed by proposing an algorithm Cluster based load balancing which works well in heterogeneous nodes environment, considers resource specific demands of the tasks and reduces scanning overhead by dividing the machines into clusters. Experimental results have shown that our algorithm gives better results in terms of waiting time, execution time, turnaround time and throughput as compared to existing throttled and modified throttled algorithms.