Parallel Loop Scheduling Using Knowledge-Based Workload Estimation on Grid Environments

Parallel loop scheduling on grid environments is a challenging problem, especially for loops with irregular workload distribution. In the past, this problem of load imbalance resulting from irregular workload was not explicitly addressed. This paper proposes a new approach to schedule loop iterations with irregular workload on grid environments. Based on knowledge-based estimation of workload, the proposed method can dispatch an appropriate proportion of workload to each node for execution according to its performance. In addition, the scheduler uses historical statistics of CPU usage and network bandwidth to estimate the dynamically changing performance of each node. Two applications, regular type and irregular one respectively, are implemented and executed on a grid test-bed, which consists of four schools. Experimental results show that the new approach improves the performance on previous schemes