Small-Time Scale Network Traffic Prediction Using Complex Network Models

The self-similar and nonlinear nature of network traffic makes high accurate prediction difficult. Various technology, including Autoregressive Integrated Moving Average (ARIMA), Local Approximation (LA), Neural Network (NN) etc., have been applied to internet traffic prediction. In this paper, Complex Network based on genetic programming and particle swarm optimization is proposed to predict the time series of internet traffic.We propose an automatic method for constructing and evolving our complex network model. The structure of complex network is evolved using genetic programming, and the fine tuning of the parameters encoded in the structure is accomplished using particle swarm optimization algorithm. The relative performances of our model are reported. The results show that our model has high prediction accuracy and can characterize real network traffic well.