Optimized CQF Scheduling in TSN: A Formal Architecture-Based Neuro-Tabu Optimized Scheduling Algorithm

Efficient real-time communication in Time-Sensitive Networking (TSN) relies on precise flow scheduling to meet stringent latency and reliability requirements. However, under the Cyclic Queuing and Forwarding (CQF) model, existing scheduling algorithms face challenges in resource allocation efficiency and the scheduling of unstable flows, leading to inconsistent performance across complex network environments. To address these challenges, firstly, this paper proposes a Formal Scheduling Architecture for CQF (CQF-FSA), which rigorously defines key scheduling elements and constraints, providing a basic, consistent, and reusable architecture for scheduling algorithms across diverse network environments; Secondly, based on CQF-FSA, we propose an optimized scheduling algorithm, NTOS (Neuro-Tabu Optimized Scheduler), which combines the global exploration capabilities of NEAT (NeuroEvolution of Augmenting Topologies) with the local optimization efficiency of Tabu search. NTOS effectively overcomes the limitations of existing methods by optimizing resource utilization and reducing scheduling conflicts; Finally experimental results demonstrate that NTOS improves the scheduling success rate by an average of 34.5% over the NV algorithm and 3.23% over the state-of-the-art MSS algorithm across various network topologies. This paper provides a highly optimized solution for CQF scheduling in TSN, significantly enhancing scheduling efficiency and scalability.