Adaptive spatio-temporal graph convolutional network with attention mechanism for mobile edge network traffic prediction

Abstract

In the current era of mobile edge networks, a significant challenge lies in overcoming the limitations posed by limited edge storage and computational resources. To address these issues, accurate network traffic prediction has emerged as a promising solution. However, due to the intricate spatial and temporal dependencies inherent in mobile edge network traffic, the prediction task remains highly challenging. Recent spatio-temporal neural network algorithms based on graph convolution have shown promising results, but they often rely on pre-defined graph structures or learned parameters. This approach neglects the dynamic nature of short-term relationships, leading to limitations in prediction accuracy. To address these limitations, we introduce Ada-ASTGCN, an innovative attention-based adaptive spatio-temporal graph convolutional network. Ada-ASTGCN dynamically derives an optimal graph structure, considering both the long-term stability and short-term bursty evolution. This allows for more precise spatio-temporal network traffic prediction. In addition, we employ an alternative training approach during optimization, replacing the traditional end-to-end training method. This alternative training approach better guides the learning direction of the model, leading to improved prediction performance. To validate the effectiveness of Ada-ASTGCN, we conducted extensive traffic prediction experiments on real-world datasets. The results demonstrate the superior performance of Ada-ASTGCN compared to existing methods, highlighting its ability to accurately predict network traffic in mobile edge networks.