Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domain that implement their functionalities through chains of intercommunicating tasks. This paper studies the problem of scheduling real-time DAG tasks by presenting a novel schedulability test based on the concept of trivial schedulability. Using this schedulability test, we propose a new DAG scheduling framework (edge generation scheduling -- EGS) that attempts to minimize the DAG width by iteratively generating edges while guaranteeing the deadline constraint. We study how to efficiently solve the problem of generating edges by developing a deep reinforcement learning algorithm combined with a graph representation neural network to learn an efficient edge generation policy for EGS. We evaluate the effectiveness of the proposed algorithm by comparing it with state-of-the-art DAG scheduling heuristics and an optimal mixed-integer linear programming baseline. Experimental results show that the proposed algorithm outperforms the state-of-the-art by requiring fewer processors to schedule the same DAG tasks.

翻译：有向无环图（DAG）任务目前被实时领域采用，用于建模来自汽车、航空电子和工业领域的复杂应用，这些应用通过相互通信的任务链实现其功能。本文研究了实时DAG任务的调度问题，提出了一种基于平凡可调度性概念的新颖可调度性测试。利用该可调度性测试，我们提出了一种新的DAG调度框架（边生成调度——EGS），该框架通过迭代生成边同时保证截止时间约束，旨在最小化DAG宽度。我们研究了如何高效求解边生成问题，通过开发一种结合图表示神经网络的深度强化学习算法，为EGS学习高效的边生成策略。通过将所提算法与最先进的DAG调度启发式算法以及最优混合整数线性规划基线进行比较，我们评估了其有效性。实验结果表明，所提算法在调度相同DAG任务时所需处理器数量更少，性能优于现有先进方法。