Joint Optimization of Adaptive Time Slot Resource Segmentation and Route Scheduling with CQF Mechanism in Time-Sensitive Networks

Time-sensitive networks (TSN), as an emerging technology, are designed to fulfill the need for low-latency and deterministic data transmission in areas such as drones and Industry 4.0. To simplify the computation of scheduling plans in TSN, the cyclic queue forwarding (CQF) mechanism is introduced to efficiently schedule time-triggered (TT) flows in TSN. However, these studies have generally neglected the impact of routing on scheduling and the importance of intratimeslot resource allocation for optimal flow scheduling. To this end, this paper proposes a joint routing-optimized scheduling strategy aimed at enhancing the schedulability of TT flows. This paper also designs an adaptive time slot segmentation (ATSS) mechanism, which improves resource utilization by grouping flows of different sizes and dynamically allocating resources within each time slot based on the amount of data in each group. In order to combine the ATSS mechanism with joint routing co-optimization scheduling for optimal scheduling of TT flows, a heuristic algorithm based on scoring of pathoffset pair is designed in this paper. The algorithm selects the optimal routes and offsets by scoring the path-offset pair of each time-sensitive data flow to maximize the schedulability of TT flows. To verify the effectiveness of the proposed algorithm, the algorithm is compared with several existing algorithms in different simulation scenarios. The experimental results show that the designed algorithm improves the scheduling success rate and resource utilization, ensures link load balancing, has acceptable computational overhead, and has good scalability to adapt to networks of different sizes.