Modeling and Analysis to Estimate the Performance of Heterogeneous Cluster

Abstract. The goal of load balancing is to assign to each node a number of tasks proportional to its performance. Many load balancers have been proposed that deal with applications with homogeneous tasks; but, applications with heterogeneous tasks have proven to be far more complex to handle. Load balancing techniques play a very important role in developing high-performance cluster computing platforms. Many load balancing polices achieve high system performance by increasing the utilization of CPU, memory, or a combination of CPU and memory. However, these load-balancing policies are less effective when the workload comprises of a large number of I/O-intensive tasks and I/O resources exhibit imbalanced load. The I/O intensive tasks running on a heterogeneous cluster needs effective usage of global I/O resources. We have proposed a load-balancing scheme based upon system heterogeneity and migrate I/O-intensive tasks to the fastest processor. The proposed load balancing scheme can minimizes the average slow down of all parallel jobs running on a cluster and reduces the average response time of the jobs