Optimizing TSN Routing, Scheduling, and Task Placement in Virtualized Edge-Compute Platforms

Configuring TSN network elements involves solving a (joint) routing and scheduling problem, where traditionally the TSN endpoints (i.e. talkers and listeners) are already deployed inside fixed industrial computers. However, with the emergence of edge computing on the shop floor, PLCs are becoming virtualized and can flexibly be deployed in containers or virtual machines. This additional flexibility could enhance the network configuration. In this paper, we propose GenTSN, a hybrid genetic algorithm designed to jointly optimize TSN routing, scheduling, and placement of TSN tasks (i.e. talkers and listeners) in virtualized Edge-Compute Platforms. We evaluate GenTSN, showing its efficiency compared to state of the art scheduling algorithms. In particular, we demonstrate that additional degrees of freedom to flexibly place TSN tasks or to flexibly route the traffic leads to better performance.