Spatio-temporal graph learning: Traffic flow prediction of mobile edge computing in 5G/6G vehicular networks

Mobile Edge Computing (MEC) is a key technology that emerged to address the increasing computational demands and communication requirements of vehicular networks. It is a form of edge computing that brings cloud computing capabilities closer to end-users, specifically within the context of vehicular networks, which are part of the broader Internet of Vehicles (IoV) ecosystem. However, the dynamic nature of traffic flows in MEC in 5G/6G vehicular networks poses challenges for accurate prediction and resource allocation when aiming to provide edge service for mobile vehicles. In this paper, we present a novel approach to predict the traffic flow of MEC in 5G/6G vehicular networks using graph-based learning. In our framework, MEC servers in vehicular networks are construed as nodes to construct a dynamic similarity graph and a dynamic transition graph over a duration of multiple days. We utilize Graph Attention Networks (GAT) to learn and fuse the node embeddings of these dynamic graphs. A transformer model is subsequently employed to predict the vehicle frequency accessing the edge computing services for the next day. Our experimental results have shown that the model achieves high accuracy in predicting edge service access volumes with low error metrics.