Network Traffic Prediction Using Integrated Deep Graph Neural Network Based on Big Data

ABSTRACT Big data are difficult to process because of its volume and frequent updates. Big data are used to predict network traffic, which allows for further analysis at the application level. Network traffic prediction is essential for effective network planning and management. Deep learning (DL) has emerged as an effective way of capturing complex spatiotemporal relationships, with graph neural network (GNN) models being especially popular in this area. Nonetheless, conventional GNN techniques have inefficiencies in long‐term forecasting in network traffic prediction, resulting in suboptimal predictive performance. To overcome the difficulties in forecasting network traffic, an integrated deep graph neural network (DeepGNN) model is presented in this work. First, create an integrated learning module that takes advantage of spatial correlation. Furthermore, sequence convolutional neural networks (sequence convolutional neural network [CNN]) are used for nonlinear dependencies, whereas attention mechanism incorporation is designed for heterogeneous features. In this study, integrated DeepGNN is evaluated on two network traffic datasets, Milan and Trentino. Three services, SMS, call, and internet, are also included for evaluation services in the first dataset and cumulative services in the second. Integrated DeepGNN is compared with the various existing models considering mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The proposed technique achieves a 4.923 MAE rate, which is lower than other techniques. The performance of the proposed technique is analyzed and compared with some related techniques to describe the superiority of the proposed model.