Flow-Aware Scheduling with Graph Neural Network Routing for Resource Efficiency in Time-Sensitive Networking

Time-Sensitive Networking (TSN) emerges as a promising technique offering deterministic transmission through effective scheduling mechanisms. However, the coexistence of non-critical flows and time-sensitive flows in TSN presents a predicament as the expansion of solution space renders it arduous to solve. It also faces the dilemma of lacking adaptability to dynamic network environments due to limited information under increasingly complex requirements. To attack this problem, a flow-aware scheduling with graph neural network routing for resource efficiency in TSN, named DTRJ, is proposed. DTRJ utilizes a multi-agent deep reinforcement learning (DRL) method to dynamically allocate queue resources for flows, adjust link weights to enhance network load balancing, and jointly achieve resource allocation optimization. The flow-aware strategy and lightweight neural network of the scheduling agent, combined with the link-level features extraction ability of graph neural network-based routing agent, contribute to the effectiveness of DTRJ in improving network performance. Specifically, DTRJ introduces a parallel design for joint scheduling and routing tasks, enabling real-time decision-making in multi-agent systems to expedite computation. Experimental results show that DTRJ outperforms the state-of-the-art Tabu/DRL methods in resource utilization, while maintaining robust performance across various topologies and flow periods, achieving an average schedulability of 91%.