Dynamic routing method in mobile software-defined networks using artificial intelligence

The article examines the problem of ensuring effective routing and load balancing in mobile Software-Defined Networks (SDN), which are characterized by high node mobility and frequent topology changes. Existing solutions based on classical static approaches or basic artificial intelligence models (for example, multilayer perceptron) have a significant drawback: they analyze the network state as a snapshot at a fixed moment in time and are unable to capture temporal changes and the evolution of the topology graph. This leads to SDN controller overload and reduced Quality of Service (QoS) due to constant link breaks. To solve this problem, a conceptual architecture for a predictive routing system based on Temporal Graph Neural Networks (T-GNN) is proposed. The suggested approach allows for modeling spatio-temporal dependencies in the network, predicting the future state of connections based on historical data, and establishing routes proactively, which significantly reduces the load on the control plane.