Reinforcement Learning assisted Routing for Time Sensitive Networks

Bülbül, Nurefşan Sertbas; Fischer, Mathias

doi:10.1109/globecom48099.2022.10001630

Cited by 3 publications

(1 citation statement)

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“…While the results show improvements over a shortest path approach, the formulation is tailored to the TSN schedulers and the optimality of the solution is difficult to assess. More recently, [11] applied reinforcement learning to routing for routing for TSN to meet flow deadlines. Their method is based on packet-level simulation of the network, effectively giving no guarantees about the network delays computed.…”

Section: Related Workmentioning

confidence: 99%

Network Synthesis under Delay Constraints: The Power of Network Calculus Differentiability

Geyer

Bondorf

2022

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications

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With the advent of standards for deterministic network behavior, synthesizing network designs under delay constraints becomes the natural next task to tackle. Network Calculus (NC) has become a key method for validating industrial networks, as it computes formally verified end-to-end delay bounds. However, analyses from the NC framework have been designed to bound the delay of one flow at a time. Attempts to use classical analyses to derive a network configuration have shown that this approach is poorly suited to practical use cases. Consider finding a delay-optimal routing configuration: one model had to be created for each routing alternative, then each flow delay had to be bounded, and then the bounds had to be compared to the given constraints. To overcome this three-step process, we introduce Differential Network Calculus. We extend NC to allow the differentiation of delay bounds w.r.t. to a wide range of network parameters -such as flow paths or priority. This opens up NC to a class of efficient nonlinear optimization techniques that exploit the gradient of the delay bound. Our numerical evaluation on the routing and priority assignment problem shows that our novel method can synthesize flow paths and priorities in a matter of seconds, outperforming existing methods by several orders of magnitude.

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Section: Related Workmentioning

confidence: 99%