An Integrated Prediction Model for Network Traffic based on Wavelet Transformation

Lei Ding, Jinhua She, Sheng Peng

Abstract


To deal with the characters with the changing trend of the steady state and dynamic state of network traffic, an integrated prediction model for network traffic based on wavelet transformation is presented in this paper. First, the network traffic is decomposed using wavelet transformation and is reconstructed using a single branch algorithm. Next, the low frequency components of the network traffic are predicted using an improved gray theory. They are used to depict the changing trend of the steady state. Then, the high frequency components of the network traffic are predicted using a BP neural network algorithm. They are used to reveal the dynamic effect. Finally, all those results are synthesized to predict the network traffic. Simulation results show that the presented integrated model increased the prediction accuracy and decreased the negative effect brought by the burstiness and uncertainty.

DOI: http://dx.doi.org/10.5755/j01.eee.19.3.3700


Keywords


Wavelet transforms; prediction methods; communication system traffic; neural networks

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Print ISSN: 1392-1215
Online ISSN: 2029-5731