RhythmScheduler: Resilient TSN Scheduling under Temporal Uncertainty and Topology Churn

Time-Sensitive Networking (TSN) underpins time-critical industrial systems with ultra-low-latency, deterministic communication. However, long-term observations in real factories reveal that temporal uncertainty from legacy devices (jitter and clock drift) and topology churn from reconfigurable workflows break the assumptions of existing TSN schedulers, sharply degrading their performance. Therefore, we present RhythmScheduler, a fast, scalable TSN scheduler that explicitly models jitter and drift via three components: a temporal–stochastic graph encoder capturing timing variation and topology; a dual-head Transformer decoupling offset selection from guard-band allocation; and a Bayesian meta-adaptation loop on a hybrid digital twin, achieving sub-second reconfiguration with 3–4 ms inference. Extensive experiments on both simulation and hardware-in-the-loop testbeds show that RhythmScheduler improves schedulability by 12.2%/25.9%, reduces guard-band waste by 55.6%/62.6%, and generates Gate Control Lists (GCLs) 11.2×/14.8× faster on 200-link networks compared to state-of-the-art RL/heuristics-based methods; and achieves a further 52.9% schedulability gain, 52.5% waste reduction, and 10.9× speedup on 400-link networks, where prior RL baselines fail due to resource exhaustion. By combining efficiency with robustness, RhythmScheduler bridges deterministic TSN theory and noisy shop-floor reality, removing traffic scheduling as a bottleneck to agile industrial automation.