DocumentCode :
3357922
Title :
Multiscale analysis and prediction of network traffic
Author :
Zhao, Hong
Author_Institution :
Fairleigh Dickinson Univ., Teaneck, NJ, USA
fYear :
2009
fDate :
14-16 Dec. 2009
Firstpage :
388
Lastpage :
393
Abstract :
Traffic prediction plays an important role in network management especially for the current networks that do not comply to the Poisson model. Wavelet transform is an emerging technique that has a significant advantage in analyzing time domain signals. When combined with LMS (Least Mean Square), wavelet based predictor can achieve better performance than time domain predictor for self similar traffic which are revealed as the current network traffic. However, the computational complexity in predicting each wavelet coefficient is high. In this paper, first, the Least Mean Kurtosis (LMK) which uses the negated kurtosis of the error signal as the cost function, is proposed to estimate wavelet coefficients; then by analyzing the wavelet coefficients of two consecutive data sets, a fast WLMK is proposed to reduce the computational complexity. Simulation results show that the fast WLMK not only incurs smaller prediction error but also reduces the computational complexity greatly.
Keywords :
computational complexity; least mean squares methods; telecommunication network management; telecommunication traffic; time-domain analysis; wavelet transforms; LMS method; WLMK; computational complexity; least mean kurtosis; network traffic multiscale analysis; network traffic multiscale prediction; telecommunication network management; time domain predictor; time domain signals analysis; wavelet transform based least mean square method; Computational complexity; Predictive models; Signal analysis; Telecommunication traffic; Time domain analysis; Traffic control; Wavelet analysis; Wavelet coefficients; Wavelet domain; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Performance Computing and Communications Conference (IPCCC), 2009 IEEE 28th International
Conference_Location :
Scottsdale, AZ
ISSN :
1097-2641
Print_ISBN :
978-1-4244-5737-3
Type :
conf
DOI :
10.1109/PCCC.2009.5403856
Filename :
5403856
Link To Document :
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