GTT-NTP: A Graph Convolutional Networks-Based Network Traffic Prediction model

The advent of 5G technology portends a significant increase in Internet of Things (IoT) connectivity, with estimates suggesting over 29 billion devices globally by 2027. This surge underscores the necessity for precise network traffic forecasting in network management. Our study addresses this gap by introducing an Artificial Intelligence (AI)-based forecasting method that amalgamates the Graph Convolutional Network (GCN), Transformer’s encoder, and Temporal Convolutional Network (TCN) components. The GCN, adept at processing network structures, is utilized to discern complex network topologies and spatial dependencies. To encapsulate the temporal dynamics of network traffic, the Transformer encoder is integrated with TCN, enabling simultaneous extraction of both global and local temporal attributes. The performance of our proposed model is rigorously tested on simulated datasets, representing diverse network intensities, derived from GEANT2 and NSFNET frameworks via OMNet++. These simulations focus on one-hop link predictions to evaluate the efficacy of the model. Our findings attest to the model’s robust capability, showcasing its proficiency in accurate network traffic prediction within an IoT milieu dominated by 5G technology.