A Capacity-based Load Balancing and Job Migration Algorithm for Heterogeneous Computational Grids

This paper addresses the problem of scheduling and load balancing in heterogeneous computational grids. We proposed a two-level load balancing policy for the multi-cluster grid environment where computational resources are dispersed in different administrative domains or clusters which are located in different local area networks. The proposed load balancing policy takes into account the heterogeneity of the computational resources. It distributes the system workload based on the processing elements capacity which leads to minimize the overall job mean response time and maximize the system utilization and throughput at the steady state. To evaluate the performance of the proposed load balancing policy, an analytical model is developed. The results obtained analytically are validated by simulating the model using Arena simulation package. The results show that the overall mean job response time obtained by simulation is very close to that obtained analytically. Also, the simulation results show that the performance of the proposed load balancing policy outperforms that of the Random and Uniform distribution load balancing policies in terms of mean job response time. The maximum improvement ratio obtained when the system workload is low. It decreases slowly as the system workload gradually increases and it decreases rapidly when the system arrival rate λ approaches the system processing rate μ because the system gradually approaches its saturation point.