  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.

Networking and Internet Architecture;Systems and Control;Systems and Control