Industrial Time-Sensitive Networking (TSN) provides deterministic mechanisms for real-time and reliable flow transmission. Increasing attention has been paid to efficient scheduling for time-sensitive flows with stringent requirements such as ultra-low latency and jitter. In TSN, the fine-grained traffic shaping protocol, cyclic queuing and forwarding (CQF), eliminates uncertain delay and frame loss by cyclic traffic forwarding and queuing. However, it inevitably causes high scheduling complexity. Moreover, complexity is quite sensitive to flow attributes and network scale. The problem stems in part from the lack of an attribute mining mechanism in existing frame-based scheduling. For time-critical industrial networks with large-scale complex flows, a so-called hyper-flow graph based scheduling scheme is proposed to improve the scheduling scalability in terms of schedulability, scheduling efficiency and latency & jitter. The hyper-flow graph is built by aggregating similar flow sets as hyper-flow nodes and designing a hierarchical scheduling framework. The flow attribute-sensitive scheduling information is embedded into the condensed maximal cliques, and reverse maps them precisely to congestion flow portions for re-scheduling. Its parallel scheduling reduces network scale induced complexity. Further, this scheme is designed in its entirety as a comprehensive scheduling algorithm GH^2. It improves the three criteria of scalability along a Pareto front. Extensive simulation studies demonstrate its superiority. Notably, GH^2 is verified its scheduling stability with a runtime of less than 100 ms for 1000 flows and near 1/430 of the SOTA FITS method for 2000 flows.

翻译：工业时间敏感网络（TSN）为实时可靠流传输提供了确定性机制。针对具有超低时延与抖动等严苛需求的时间敏感流，高效调度问题日益受到关注。在TSN中，细粒度流量整形协议——循环排队与转发（CQF）通过循环流量转发与排队机制消除了不确定时延与帧丢失，但不可避免地导致高调度复杂度。此外，调度复杂度对流的属性及网络规模高度敏感。该问题部分源于现有基于帧的调度机制缺乏属性挖掘能力。针对具有大规模复杂流的工业时间敏感网络，提出了一种基于超流图的调度方案以提升调度在可调度性、调度效率及时延-抖动方面的可扩展性。该方案通过聚合相似流集合构建超流节点，并设计分层调度框架。将流属性敏感的调度信息嵌入凝聚后的最大团结构中，并精确反向映射至拥塞流片段以进行重调度。其并行调度机制可有效降低由网络规模引发的复杂度。进一步地，该方案被整体设计为综合性调度算法GH²，该算法沿帕累托前沿提升可扩展性的三个评判标准。大量仿真研究证明了其优越性。值得注意的是，GH²在1000条流场景下运行时间低于100毫秒，在2000条流场景下运行时间约为当前最优FITS方法的1/430，验证了其调度稳定性。