Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning

TL;DR

This paper introduces a novel deep reinforcement learning-based framework for scheduling DAG tasks in real-time systems, minimizing processor usage while ensuring deadlines, and demonstrating superior performance over existing heuristics.

Contribution

It presents a new DAG scheduling framework (EGS) using deep reinforcement learning to generate edges efficiently, improving scheduling performance over state-of-the-art methods.

Findings

Outperforms existing heuristics in processor efficiency

Uses deep reinforcement learning with graph neural networks

Achieves near-optimal scheduling with fewer resources

Abstract

Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domains 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. The code is available at https://github.com/binqi-sun/egs.