Dynamic Load-Balancing of Image Processing Applications on Clusters of Workstations

Abstract Cluster-based computing, which exploits the aggregate power of a network of workstations, has drawn increasing attention from the parallel processing community. The main problem with this computing environment is the permanently changing workload of individual workstations which makes the efficiency and the execution time of parallel applications unpredictable. In this paper, we introduce an efficient load balancing scheme which aims at dynamically balancing the workload of data parallel applications in this computing environment. Simulation and experimental studies of our load balancing strategy are performed under various load situations and it is shown that it can effectively balance the workload among the workstations involved. Further, it was shown that a significant improvement in computing performance can be achieved when using our load balancing strategy as compared to the case where no load balancing is applied, particularly under a heavily loaded system.