Effective Routing for Hybird Service Flow in TSN: A Multi-Objective Optimization Approach

The development of immersive video service and large-scale cluster computing technology further expand the potential application scope of time-sensitive networks (TSN). In the delivery network for these emerging services, the Ultra-Service Flow (USF), which is characterized by ultra-high bandwidth and deterministic latency has become the most representative traffic type. Therefore, the route scheduling for hybrid deployment of Regular Service Flow (RSF) and USF has become an unavoidable issue within a deterministic domain. To resolve this issue, a multi-objective optimization model for joint routing of hybrid service flows is studied in this paper. Subsequently, an effective algorithm is designed to discover feasible routing solutions using a deep reinforcement learning approach with a transformer framework, followed by optimization utilizing NSGA-II. The simulation results indicate that, our proposed algorithm exhibits superior overall performance and enhanced generalization capabilities. It effectively reduces the overall latency of RSF by 10.526% and the path blocking degree of USF by 14.10256%, while significantly increasing the available bandwidth rate by 14.286%.