Learning-Based Scalable Scheduling and Routing Co-Design With Stream Similarity Partitioning for Time-Sensitive Networking
Lei Xu, Qimin Xu, Jingzheng Tu, Jinglong Zhang, Yanzhou Zhang, Cailian Chen, Xinping Guan
Abstract
The deterministic and real-time communication is the indispensable requirement in Industrial Internet of Things (IIoT) application areas. Time-sensitive networking (TSN) is a promising technology for this kind of communication demands through designing proper scheduling and routing mechanisms. However, it is still challenging to design the mechanisms for large-scale instances due to high computational complexity. In order to guarantee schedulability and scalability, a learning-based scalable scheduling and routing co-design (LSSR) architecture is proposed in this article for TSN. A stream partition method combining classification and graph-based clustering is established to reduce interpartition conflicts to enhance schedulability based on the explored domain knowledge and the characterized stream data set for practical requirements. Integrated with the stream partition method, we construct the constraints of scheduling and routing co-design to guarantee the deterministic and real-time transmission. An iterative scheduling algorithm is proposed to reduce the computational complexity and thus, to enhance scalability. Simulations demonstrate the effectiveness and advantages of the proposed LSSR scheme.