A Graph Attention Network Approach to Partitioned Scheduling in Real-Time Systems

Abstract

Machine learning methods have been used to solve real-time scheduling problems but none has yet made an architecture that utilizes influences between real-time tasks as input features. This letter proposes a novel approach to partitioned scheduling in real-time systems using graph machine learning. We present a graph representation of real-time task sets that enable graph machine-learning schemes to capture the influence between real-time tasks. By using a graph attention network (GAT) with this method, our model successfully partitioned-schedule task sets that were previously deemed unschedulable by state-of-the-art partitioned scheduling algorithms. The GAT is used to establish relationships between nodes in the graph, which represent real-time tasks, and to learn how these relationships affect the schedulability of the system.