A Novel Load Balancing Algorithm for I/O-intensive Load in Heterogeneous Cluster

Abstract

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 loadbalancing scheme based upon system heterogeneity and migrate I/O-intensive tasks from with heavily loaded nodes to under loaded nodes. 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.