Joint optimization of time-slot allocation and traffic steering for large-scale deterministic networks

Recently, time-sensitive services have expanded from traditional industrial control systems to more scenarios. Some time-sensitive applications, such as remote surgery, autonomous driving, augmented reality (AR), etc., require deterministic end-to-end delay and jitter in data transmission. deterministic network (DetNet) is proposed as a promising technology for providing deterministic service in wide area networks (WAN). DetNet guarantees deterministic end-to-end delay and jitter by specifying a certain routing path and transmission time-slots for timesensitive flows. However, how to efficiently steer time-sensitive flows while jointly allocating transmission time-slots is still an open problem. Existing flow scheduling algorithms are limited in the scenarios of local area networks (LAN), and do not consider the impact of propagation delay in large-scale networks. To this end, we study the joint optimization of time-slot allocation and traffic steering, while considering the propagation delay of WAN links. Our objective is to maximize the number of successfully deployed time-sensitive flows under the constraints of required end-to-end delay. Accordingly, we formulate the studied problem as an integer linear programming (ILP) model. Since it is proved to be an NP-hard problem, we design a heuristic algorithm named genetic-based deterministic network traffic scheduling (GDNTS). The solution with the largest number of deployed time-sensitive flows can be obtained from the evolution of chromosomes in GDNTS. Compared with the benchmark algorithms, extensive simulation results show that GDNTS improves the deployed time sensitive-flows number by 22.85% in average.