DRL-based automation of Time Sensitive Networks (TSN)

Abstract

This Master Thesis addresses the routing and scheduling assignment problem of Time Sensitive Networks (TSN), a set of standards that IEEE defined to provide low-latency reliable communications over Ethernet networks. The proposed solutions have been based on Deep Reinforcement Learning (DRL), a subset of Machine Learning that is very powerful in solving complex sequential decision-making problems. This work is part of the 6GSMART-EZ project, which aims to develop the integration of 5G and TSN networks, so one of the proposed solutions complies with this integration scenario. First, some literature research is conducted to identify the problem to solve and be able to propose adequate solutions. Second, a centralised approach of DRL models has been implemented and tested on a simulated isolated private TSN network to support simple deployments that do not require any integration with 5G networks. Third, a distributed approach with an agent at each side of the 5G network has also been implemented. This approach proposed a network topology with two TSN networks integrated with a 5G network by creating two interconnection points.