A Global DAG Task Scheduler Using Deep Reinforcement Learning and Graph Convolution Network

Parallelization of tasks and efficient utilization of processors are considered important and challenging in operating large-scale real-time systems. Recently, deep reinforcement learning (DRL) was found to provide effective solutions to various combinatorial optimization problems. In this paper, inspired by recent achievements in DRL, we employ DRL techniques for scheduling a directed acyclic graph (DAG) task in which a set of non-preemptive subtasks are specified by precedence conditions among them. We propose a DRL-based priority assignment model for scheduling a DAG task on a multiprocessor system, named GoSu, which adapts a graph convolution network (GCN) to process a complex interdependent task structure and minimize the makespan of a DAG task. Our proposed model makes use of both temporal and structural features in a DAG to effectively learn a priority-based scheduling policy via GCN and policy gradient methods. With comprehensive evaluations, we verify that our model shows comparable performance to several state-of-the-art DAG task scheduling algorithms, and outperforms them by 2 similar to 3% in the slowdown of achieved makespans particularly in nontrivial system configurations where workloads are neither too small nor heavy compared to the given number of processors. We also analyze the priority assignment behaviors of our model by leveraging a regression method that imitates the learned policy of the model.