Resource-Aware Online Traffic Scheduling for Time-Sensitive Networking

Traditional Ethernet technology struggles to meet the ever-increasing demands of modern cyber-physical systems in terms of transmission bandwidth and network distribution. Time-sensitive networking (TSN) has emerged to address these challenges by providing mechanisms for accurate clock synchronization, intelligent network configuration, and high-precision traffic scheduling. However, resource-limited TSN switches restrict the length of internal queues, necessitating improved resource utilization while ensuring successful scheduling. This article introduces an online traffic scheduling framework using deep reinforcement learning (DRL) to optimize scheduling and resource allocation in TSN. By incorporating the resource allocation quality metric, known as bandwidth satisfaction, our goal is to achieve load balancing in the network's switch queues while ensuring deterministic traffic transmission. By integrating both network protocol constraints and user-defined constraints, the proposed DDTA-CNN algorithm leverages a convolutional neural network (CNN) to extract flow features and allocate sending time slots, maximizing queue resource utilization. Evaluations under various TSN settings demonstrate that DDTA-CNN significantly outperforms state-of-the-art algorithms, SMT and Tabu, by 10× and 31.8% in terms of resource allocation quality, and by 23.5% and 13.6% in terms of schedulable flows. Our framework improves network resource utilization, ensures real-time transmission, and achieves higher scheduling success rates, addressing the critical need for efficient and scalable traffic scheduling in TSN environments.