Communication-efficient Multi-service Mobile Traffic Prediction by Leveraging Cross-service Correlations

Mobile traffic prediction plays a crucial role in enabling efficient network management and service provisioning. Traditional prediction approaches treat different mobile application services (such as Uber, Facebook, Twitter, etc) as isolated entities, neglecting potential correlation among them. Moreover, such isolated prediction methods necessitate the uploading of historical traffic data from all regions to forecast city-wide traffic, resulting in consuming substantial bandwidth resources and risking prediction failure in the event of data loss in specific regions. To address these challenges, we propose a novel Cross-service Attention-based Spatial-Temporal Graph Convolutional Network (CsASTGCN) for precise and communication-efficient multi-service mobile traffic prediction. Our methodology allows each mobile service to transmit the traffic data of only a fraction of regions for city-wide traffic prediction of all mobile services, which reduces the resource consumption caused by data transmission. Specifically, the sparse traffic data are initially transmitted to the cloud server and the masked graph autoencoder is utilized to roughly reconstruct the traffic volume for regions with missing data. Subsequently, a cross-service attention-based predictor is designed to calculate the data correlation among different mobile services within the same region. Considering the constantly emerging mobile services, we incorporate a novel model-based adaptive transfer learning scheme to extract valuable knowledge from the existing models and expedite the training of a new model for a new service without training from scratch, thereby enhancing the scalability of our framework. Extensive experiments conducted on a large-scale real-world mobile traffic dataset demonstrate that our model greatly outperforms the existing schemes, enhancing both the communication-efficiency and robustness of large-scale multi-service traffic prediction.