A Scalable Hypergraph Framework for Time-Sensitive Network Scheduling Via Dynamic Sparsification and Iterative Search

The scalability of joint routing and scheduling for time-triggered (TT) traffic in large-scale Time-Sensitive Networks (TSN) has become a critical bottleneck constraining the deployment scale of network applications. To address this challenge, this paper proposes the Dynamic Hypergraph Sparsification and Search (DHSS) framework, which innovatively transforms this scheduling problem into a Maximum Independent Set (MIS) problem on a dynamically constructed Hyper Conflict Graph (HCG). Addressing the NP-hard nature of this problem, we devise a novel semi-static sparsification architecture. This architecture decouples problem precision and solving efficiency by maintaining a ground-truth hypergraph (Hgt) for accurate constraint verification and a sparse working hypergraph (Hwork) for efficient computation. We employ a strategy combining periodic global Semidefinite Programming (SDP) reconstruction with low-overhead local updates to dynamically maintain the quality of the working hypergraph, thereby achieving an effective trade-off between solution quality and computational complexity. Comprehensive experimental evaluations under various network topologies and traffic loads demonstrate that the DHSS framework significantly outperforms current state-of-the-art methods in terms of both schedulability and scalability, offering an efficient and viable solution for the automated planning of large-scale industrial networks.