Knowledge-Aware Schedulability Analysis for Time-Sensitive Networking: A GNN-Based Method

Abstract

Industrial automation is rapidly evolving toward flexible production. This transition requires networks to ensure the deterministic transmission of varying traffic sets across different production stages. Consequently, the system must be capable of rapidly analyzing whether fixed network resources can accommodate all service requirements prior to actual scheduling. While Time-Sensitive Networking (TSN) provides the deterministic transmission for such environments, existing schedulability assessments rely on exhaustive scheduling tests. However, the scheduling process is inherently an NP-hard constraint satisfaction problem, whose heavy computational overhead severely limits deployment agility. Therefore, it is critical to develop a method that can rapidly predict the constraint satisfiability of diverse traffic sets without repetitive and time-consuming scheduling. In this work, given the inherent graph-structured nature of network infrastructure and traffic patterns, we design a graph neural network model to explicitly capture complex spatial dependencies. As node attributes, sparse basic traffic features are distilled as expert knowledge and integrated, thereby enhancing prediction accuracy. When a traffic set is deemed unschedulable, we explore the traffic features and links with the greatest impact. Based on this, a feature-driven rerouting strategy is proposed to find a more schedulable traffic behavior. The evaluation results show that the model demonstrates the capability to process thousands of datasets within hundreds of microseconds, while guaranteeing a prediction accuracy of over 90% and an increase in the count of schedulable flows by about 25% compared to the standard Dijkstra’s shortest path algorithm baseline.