Network Digital Twin for Congestion-Aware Predictive Traffic Routing using Graph MPNNs
This work addresses the need for proactive, scalable routing in telecom networks to mitigate congestion and performance degradation, but the results are demonstrated only on synthetic topologies without real-world validation.
The paper proposes a Network Digital Twin (NDT) using Message Passing Neural Networks (MPNNs) for congestion-aware predictive traffic routing, achieving real-time adaptation and optimal traffic distribution without disrupting active services.
Telecom networks scale with growing users and data-intensive applications, generating heavy traffic that causes congestion, reducing throughput, increasing delay, and raising computational costs. Traditional routing protocols act only after performance degradation, making them unsuitable for dynamic traffic and topological changes. Addressing these challenges requires a routing approach that adapts in real time, scales with network growth, operates without disrupting active services, and provides continuous feedback for congestion-aware traffic optimisation. The Network Digital Twin (NDT) addresses these needs by mirroring global network behaviour using Message Passing Neural Networks (MPNNs) through bidirectional communication with the physical network. To align the NDT with physical network behaviour, synthetic traffic is generated with increasing load across topological structures that incrementally scale as routers are added. These topologies are created by graph-generating models such as Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz, customised with vertex degree limitations. The NDT collects performance metrics from routers and links, and MPNNs classify edges based on local vertex and global network behaviours. Based on these classifications, feedback is sent as Policy-Based Routing (PBR) protocol commands to each router, enabling optimal traffic distribution across links of the physical network.