Hybrid traffic scheduling in time-sensitive networking for the support of automotive applications

Time-sensitive networking (TSN) is considered one of the most promising solutions to address real-time scheduling in in-vehicle network due to its capabilities for providing deterministic service. The TSN working group proposed various traffic shaping mechanisms, while deterministic scheduling of hybrid traffic is still not effectively solved since the traffic requirements are difficult to satisfy by standalone or combined mechanisms with fixed time slot divisions. This article presents a time-aware multi-cyclicqueuing and forwarding scheduling model, that integrates the no-wait enabled time-aware shaper and multi-cyclic queuing and forwarding shaping models. Then, a scheduling solution, dubbed "TSN scheduling optimizer" (TSO) is proposed that combines optimization methods and incremental techniques. TSO aims to balance the load to maximize flow schedulability while guaranteeing the service requirements of hybrid traffic. Simulation evaluations through OMNeT++ provide a performance assessment of this proposed scheduling model, which can satisfy multiple types of traffic transmission requirements. Furthermore, TSO is compared with other baseline scheduling solutions, and TSO shows efficacy regarding execution time and schedulability. With the rapid development of the automotive field, numerous advanced driving applications are widely employed, which require high data transmission rate, low latency, and tight coordination between sensing and communication to improve deterministic service. To tackle the hybrid traffic co-network transmission in automotive networks, a hybrid traffic scheduling model named time-aware multi-cyclic-queuing is proposed and forwarding to meet the qualification of service requirements for different traffic. Besides, a scheduling technique for load balancing and improving traffic scheduling while maintaining acceptable execution times, dubbed "time-sensitive networking scheduling optimizer", is proposed.image