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 (ENN), to predict one-step-ahead value of the MPEG4 and H.263 video, TCP traffic data. The LNN 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), LNN 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.
network traffic ANN prediction
Feng Hai-liang Chen Di Lin Qing-jia Chen Chun-xiao
School of Information Science and Engineering,Shandong University Jinan,250100,China
国际会议
武汉
英文
2006-09-01（万方平台首次上网日期，不代表论文的发表时间）