Scheduling directed a-cyclic task graphs on a bounded set of heterogeneous processors using task duplication

In a distributed computing environment, the schedule by which tasks are assigned to processors is critical to minimizing the overall run-time of the application. However, the problem of discovering the schedule that gives the minimum finish time is NP-Complete. This paper addresses static scheduling of a directed a-cyclic task graph (DAG) on a heterogeneous, bounded set of distributed processors to minimize the makespan. By combining several innovative techniques, including insertion-based scheduling and multiple task duplication, we present a new heuristic, known as Heterogeneous N-predecessor Decisive Path (HNDP), for scheduling directed a-cyclic weighted task graphs (DAGs) on a set of heterogeneous processors. We compare the performance of HNDP, under a range of varying input conditions, with two of the best existing heterogeneous heuristics namely HEFT and STDS. The results presented in this paper show that HNDP outperforms the two heuristics in terms of finish time and the number of processors employed over a wide range of parameters. The complexity of HNPD is O(v2.p) vs. O(v2.p) of HEFT and O(v2) of STDS where v is the number of nodes in the DAG.