Network State Aware Dual-Graph Spatiotemporal Fusion Prediction Model for SDN Dynamic Routing Optimization

Software-defined networking (SDN) provides a flexible solution to manage complex networks on demand by centralized control and programmability. However, efficiently optimizing network configurations to achieve load balance and improve service quality remains challenging. In this paper, we propose a novel SDN network state awareness and dynamic routing optimization method, termed DGSFN-DR. Hereby, we leverage a Graph Attention Network (GAT) to model the spatial dependencies of the network topology for its link graph. Then, we employ a Recurrent Neural Network (RNN) to capture the temporal dependencies of link states, including the lagged temporal features induced by routing algorithms, to improve the prediction accuracy of future link states. Our algorithm dynamically adjusts routing strategies to optimize network performance according to the predicted link weights with the dual graph spatiotemporal fusion prediction network (DG-SFN). Experimental results demonstrate that our DGSFN-DR outperforms other methods in various network traffic intensities and topologies. Specifically, it achieves improvements of 4% to 15% in latency, jitter, packet loss, and available bandwidth. In particular, the DGSFN-DR exhibits superior adaptability and optimization potential under high traffic loads and complex network topologies. This work expands dynamic routing optimization theory for SDN and new insights for practical network management.