A Monte-Carlo Approach for Full-Ahead Stochastic DAG Scheduling

In most heterogeneous computing systems, there is a need for solutions that can cope with the unavoidable uncertainty in individual task execution times, when scheduling DAGs. When such uncertainties occur, static DAG scheduling approaches may suffer, and some rescheduling may be necessary. Assuming that the uncertainty in task execution times is modelled in a stochastic manner, then we may be able to use this information to improve static DAG scheduling considerably. In this paper, a novel DAG scheduling approach is proposed to solve this stochastic scheduling problem, based on a Monte-Carlo method. The approach is built on the top of a classic static scheduling heuristic and evaluated through extensive simulation. Empirical results show that a significant improvement on average application performance can be achieved by the proposed approach at a reasonable execution time cost.