A Mixed-Criticality Traffic Scheduler with Mitigating Congestion for CAN-to-TSN Gateway

Author:

Yan Wenyan1ORCID,Wei Dongsheng1ORCID,Fu Bin1ORCID,Li Renfa1ORCID,Xie Guoqi1ORCID

Affiliation:

1. College of Computer Science and Electronic Engineering, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China, Changsha, China

Abstract

The network architecture that Time-Sensitive Networking (TSN) is used as the backbone network and the Controller Area Network (CAN) serves as the intra-domain network is considered as the CAN-TSN interconnection network architecture, which has gained considerable attention within industrial embedded networks, such as spacecraft, intelligent automobiles, and factory automation. The architecture employs the CAN-TSN gateway as a central hub for transmitting and managing a significant volume of communications between the CAN domains and TSN. However, the CAN-TSN gateway faces a high congestion challenge due to the rapid growth in data volume, making it difficult to effectively support different time planning mechanisms provided by TSN. In this article, we propose a two-stage mixed-criticality traffic scheduler. The scheduler in the first stage adopts a Message Optimization Algorithm (MOA) to aggregate multiple CAN messages into a single TSN message (including the aggregation of critical and non-critical CAN messages), which reduces the number of CAN messages requiring transmission. In the second stage, the scheduler proposes a Message Scheduling Optimization Algorithm (MSOA) to schedule critical TSN messages. This algorithm reassembles all the critical CAN messages (within the un-schedulable TSN messages) to generate new TSN messages for rescheduling. Experimental results show that our proposed scheduler effectively improves the acceptance ratio of critical and non-critical CAN messages and outperforms the state-of-the-art message scheduling method in terms of acceptance ratio while improving the bandwidth utilization and the number of schedule table entries. We further construct a hardware platform to evaluate the performance of MSOA. The consistency between practical results and theoretical results shows the effectiveness of MSOA.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Natural Science Foundation of Chongqing

Publisher

Association for Computing Machinery (ACM)

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