Dynamic flow scheduling optimization based on intelligent control for digital twins

Abstract

AbstractWith the rapid development of artificial intelligence, big data, cloud computing, and other technologies, supporting digital twin technology has become a research hotspot today. It expounds the modeling and application of digital twins in the field of smart cities, and studies the network in digital twin cities. The optimization model, in view of the diversity and burstiness of the traffic in the network, in order to effectively handle the increasing user access data traffic and the increasingly wide range of business types, a dynamic traffic scheduling method based on the swarm intelligence optimization algorithm is designed, and a gray‐based dynamic traffic scheduling method is proposed. Intelligent path planning algorithm optimized by wolf. The simulation shows that for different types of services, the algorithm can set different priorities based on delay/bandwidth, calculate the optimal path, suboptimal alternate path, third alternate path, and realize intelligent obstacle avoidance and intelligent decision‐making. Scheduling, so as to carry out efficient, reasonable and intelligent traffic scheduling. And in the case of a node failure on the optimal path, the optimal path based on the shortest distance can be quickly calculated.