Multi-Scale Network Traffic Prediction Using A Two-Stage Neural Network Combined Model

High-speed network traffic prediction is considered as the core of the preventive Congestion control. In this paper, we apply two different artificial neural network (ANN) architectures, linear neural network (LNN) and Elman neural network (LAW), to predict one-step-ahead value of the MPEG4 and H.263 video, TCP traffic data. The LAW predicts the linear data, whereas the ENN predicts the nonlinear data. To enhance the prediction accuracy and merge the traffic characteristics captured by individual models, the output of the individual ANN predictors are combined using averaging and three networks respectively. They are back propagation neural network (BPNN), LAW and ENN. The problem of one-step-ahead traffic prediction at different timescales is considered The results indicate that the proposed combined model outperforms the individual models. The results also show that the prediction performance depends on the traffic nature and the considered timescale.