Internet Traffic Forecasting using Temporal-Topological Graph Convolutional Networks

Accurate and timely prediction of the Internet traffic flow is important for network performance improvement. Few prediction methods incorporate the topology information of networks. In this paper, we present a novel Internet traffic forecasting algorithm named TTGCN, which applies the graph neural networks for traffic flow prediction on each link of a backbone network. The topology of the network was represented by a novel adjacency matrix, which models the relationship between links. The design makes TTGCN capable of capturing both the temporal and topological information of the traffic flow. TTGCN is validated on UKERNA, a dataset captured from a real backbone network. The experimental results show that the average error of the proposed TTGCN over 90 minutes is 28.249 (Mbit/s), which is a significant improvement of conventional models, while that of ARIMA and GRU and STGCN are 44.955, 40.935, and 36.152, respectively. The proposed TTGCN, by encoding both temporal and topological information in a neural representation, can achieve better prediction performance on network traffic.