Using adaptive linear prediction to support real-time VBR video under RCBR network service model
IEEE/ACM Transactions on Networking (TON)
Multiresolution learning paradigm and signal prediction
IEEE Transactions on Signal Processing
Predictive dynamic bandwidth allocation for efficient transport of real-time VBR video over ATM
IEEE Journal on Selected Areas in Communications
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Network traffic burst becomes a threat to network security. In this paper, a decomposition based method is presented for network burst traffic realtime prediction, in which, by passing smoothing filter, network traffic is decomposed into smooth low frequency traffic and high frequency traffic to make prediction respectively, and then a superposition result of the predictions is yielded. Based on LMS algorithm, an improvement of LMS predictor by adjusting prediction according to prediction errors (EaLMS, Error-adjusted LMS) is proposed to process the low frequency traffic, and a simple method of linear combination is presented to predict the high frequency traffic. The experiment results using real network traffic data shows, compared with traditional LMS, the prediction method based on decomposition obviously shorted the prediction delay and reduced the prediction error during traffic burst, while it also improves the global prediction.