Time Sensitive Network Scheduling Method Based on Genetic Algorithm

With the progress of network technology, applications such as vehicle networks, industrial Internet of Things and 5G ultra-reliable low-delay communication (uRLLC) all require TSN to ensure ultra-low delay deterministic data transmission. TSN traffic scheduling requires a fast and accurate scheduling algorithm. The existing accurate solution methods are of high complexity and cannot meet the real-time requirements in large-scale joint scheduling. This paper designed a routing optimization genetic algorithm (Routing-GA) with better performance. Combining routing and traffic scheduling constraints, it can improve the efficiency of scheduling algorithm by optimizing routing and provide services for link load balancing scheduling. This strategy increases the space and flexibility of scheduling, and has the characteristics of fast near-optimal solution of meta-heuristic algorithm. It can deal with large-scale TSN routing constraint joint scheduling problem simply and effectively. Routing-GA takes the minimum end-to-end delay of time-sensitive flow as the optimization objective, considers Routing and TSN constraints jointly, and provides a genetic algorithm coding method with low complexity, high efficiency and high scalability according to the characteristics of TSN transmission problems. In addition, in order to improve the performance of the scheduling algorithm, a crossover mutation mechanism was proposed to optimize the route length and link load balancing. The experimental results show that the realized Routing-GA can effectively reduce the end-to-end delay and significantly improve the solution quality. The evolution rate can reach 24.42%, and the average iteration time of traditional genetic algorithm (GA) is only 12%. It can effectively improve the performance of the algorithm and meet the constraint requirements of TSN scheduling.