Optimization of uncertain dependent task mapping on heterogeneous computing platforms

Dependent tasks are typically modeled using directed acyclic graphs (DAGs), and scheduling algorithms based on DAGs have been extensively researched. Most of the existing algorithms assume that the task or communication duration is deterministic. Nevertheless, any delays in task execution or communication can significantly affect the scheduling results. Aiming at minimizing the DAGs' makespan, a heuristic algorithms called heterogeneous optimistic complete time (HOCT) is proposed. The algorithm assumes that the task characteristic values are modeled randomly. It calculates task priorities based on the acceleration ratio and allocates computing resources using an optimistic execution timetable. Then, a Monte-Carlo simulation-based scheduling algorithm which built on the top of HOCT is proposed. Experimental results show that the proposed algorithm achieves better makespan of the stochastic DAG. It also provides a more robust scheduling solution to unpredictability than critical-path-on-a-processor, heterogeneous earliest finish time-no cross and parental prioritization earliest finish time algorithms.