Network Traffic Modeling and Prediction Using Graph Gaussian Processes

Abstract

Traffic modeling and prediction is a vital task for designing efficient resource allocation strategies in telecommunication networks. This is challenging because network traffic data exhibits complex nonlinear spatiotemporal interactions. Moreover, the data can have missing values when traffic statistic collection is unavailable in certain nodes. In this paper, we introduce a graph Gaussian Process (GP) model for this challenging problem. The GP is a Bayesian non-parametric model and highly flexible in capturing complex patterns in the data. Additionally, it provides uncertainty information which can be exploited for robust resource allocation problems. The developed graph GP model is almost free of hyper-parameter tuning, can accurately capture short-term and long-term temporal patterns and can infer missing values by learning spatiotemporal interactions among the nodes in the network. Subsequently, we approximate the intractable posterior distribution using Variational Bayes (VB) algorithm which can be efficiently implemented. Finally, we evaluate the accuracy of the proposed model for predicting the data traffic using two real-world network datasets. Our simulation results shows that the proposed model can achieve better prediction accuracy with respect to the state-of-the-art approaches.