Adaptive Configuration with Deep Reinforcement Learning in Software-Defined Time-Sensitive Networking

Time-sensitive networking (TSN) is very appealing to industrial networks due to its support for deterministic transmission based on Ethernet. The implementation of determinism typically demands for precise configuration on each output port of a TSN switch, which is complex and time-consuming. Moreover, many emerging industrial applications bring dynamic scenarios (e.g., in real-time Internet of Things), thus the configurations should change adaptively as application requirements change to provide continued determinism. In this paper, we propose a deep reinforcement learning (DRL) based adaptive configuration scheme in Software-defined time-sensitive networking (SD-TSN). The SD-TSN is a network architecture that integrates the determinism guarantees of TSN and flexible network management of software-defined networking (SDN). Based on the capability of SD-TSN, the proposed configuration scheme exploits DRL to learn from interacting with the environment for adaptive configuration. Experimental results demonstrate the effectiveness of our scheme in dynamic scenarios.