GTSNet: A Generalized Traffic Scheduler for Time-Sensitive Networking Based on Graph Neural Network

Time-sensitive networking is a promising real-time network protocol, especially because it can utilize a time awareness shaping mechanism to realize very low delay and jitter transmission of control data traffic (CDT). Nowadays, deep learning methods have been widely applied in CDT scheduling to improve the scheduling performance. However, these trained models can only schedule for fixed networking with limited change. They have low generalization and cannot adapt to diverse application situations. To address this challenge, we propose GTSNet, a novel scheduler with strong generalization capabilities. We design the deep learning model based on a graph neural network to overcome the lack of generalization performance. Moreover, we transform the problem into a continuous node classification problem to enhance the scheduling ability and generalization. Compared with existing methods(heuristic and reinforcement learning methods), GTSNet demonstrates strong generalizability for the first time and avoids more than 50% nonschedulable flows in many generalization ability evaluation cases.