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Industrial Time-Sensitive Networking (TSN) provides deterministic mechanismsfor real-time and reliable flow transmission. Increasing attention has beenpaid to efficient scheduling for time-sensitive flows with stringentrequirements such as ultra-low latency and jitter. In TSN, the fine-grainedtraffic shaping protocol, cyclic queuing and forwarding (CQF), eliminatesuncertain delay and frame loss by cyclic traffic forwarding and queuing.However, it inevitably causes high scheduling complexity. Moreover, complexityis quite sensitive to flow attributes and network scale. The problem stems inpart from the lack of an attribute mining mechanism in existing frame-basedscheduling. For time-critical industrial networks with large-scale complexflows, a so-called hyper-flow graph based scheduling scheme is proposed toimprove the scheduling scalability in terms of schedulability, schedulingefficiency and latency & jitter. The hyper-flow graph is built by aggregatingsimilar flow sets as hyper-flow nodes and designing a hierarchical schedulingframework. The flow attribute-sensitive scheduling information is embedded intothe condensed maximal cliques, and reverse maps them precisely to congestionflow portions for re-scheduling. Its parallel scheduling reduces network scaleinduced complexity. Further, this scheme is designed in its entirety as acomprehensive scheduling algorithm GH^2. It improves the three criteria ofscalability along a Pareto front. Extensive simulation studies demonstrate itssuperiority. Notably, GH^2 is verified its scheduling stability with a runtimeof less than 100 ms for 1000 flows and near 1/430 of the SOTA FITS method for2000 flows.

Scalable Scheduling for Industrial Time-Sensitive Networking: A Hyper-flow Graph Based Scheme