Reinforcement Learning for Time-Aware Shaping (IEEE 802.1Qbv) in Time-Sensitive Networks

Abstract

Industry 4.0 involves the networking of production equipment. This can be achieved thanks to the Time-Sensitive Networking (TSN) set of network standards. However, this new paradigm brings new challenges because TSN features optimization relies on the dynamic characteristics of the underlying communication network (e.g., network topology, routing strategy, critical flows requirements, etc.). This paper focuses on the case of the IEEE 802.1Qbv standard by exploring the applicability of a Deep Reinforcement Learning (DRL) approach in order to reduce the configuration time of the TSN-specific parameters, compared to exact or heuristic methods.