An Adaptive Priority-Based Heuristic Approach for Scheduling DAG Applications with Uncertainties

In the context of large-scale distributed computing environments, among various DAG scheduling heuristics aiming at coping with severe uncertainties during task execution, those ones that try to maximize the parallelism of ready tasks seems to be promising. However, most of such heuristics consider only the DAG structure when making scheduling decision. When the information about task execution time (even though might be inaccurate) is given, it might be interesting to investigate whether this information should be taken into account during scheduling, and if yes, how to. This paper presents an adaptive priority-based heuristic, which properly considers the task execution time estimate and the DAG structure attributes in the task prioritization. The evaluation result shows the proposed approach obtains better scheduling performance than existing solutions.