ANN-Based Multi-scales Prediction of Self-similar Network Traffic

Abstract

Self-similarity is a ubiquitous phenomena spanning across diverse network environments and has great effect on network performance. The Long-Range Dependence (LRD) structure in self-similar network traffic could be exploited in traffic prediction, which is very useful in resource allocation. But the traffic prediction is very difficulty because of its multi-scale and non-linear feature. Having considered these features of self-similar network traffic, the prediction algorithm with ANN is proposed in this paper. At first, ANN for the multi-scale traffic prediction is constructed. The procession of Input/Output vectors, parameter selection and training scheme are also discussed. Then the artificial traces are generated with Fractional-ARIMA model and are used in the experiments of ANN multi-scale prediction. The result shows that this algorithm can predict the self-similar network traffic at multi-scale. It is very useful to optimize network control scheme and improve network performance.