Cart (Loading....) | Create Account
Close category search window
 

WDRLS: a wavelet-based on-line predictor for network traffic

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xin Wang ; Dept. of Electr. Eng., Tsinghua Univ., Beijing, China ; Yong Ren ; Xiuming Shan

A novel predictor for network traffic, the wavelet domain recursive least-squares (WDRLS) predictor is discussed. Empirical studies have shown that network traffic possesses diverse statistical properties and exhibits a complex correlation structure characterized by short-range dependence (SRD) and long-range dependence (LRD). A challenge in predicting network traffic is how to exploit such a complex correlation structure with both high accuracy and computational efficiency. In the proposed WDRLS predictor, we use the wavelet transform to tackle these issues. Specifically, we predict the wavelet coefficients and solve the prediction of network traffic through a reverse wavelet transform. Our approach is based on the discovery that, although the network traffic has both SRD and LRD correlation structures, the corresponding wavelet coefficients are all SRD. We further assume that the SRD wavelet coefficients can be well approximated by a linear correlation structure for prediction. A least-squares method is adopted to make predictions in the wavelet domain. An important feature about the WDRLS predictor is that it can make an on-line prediction of network traffic. This is made possible by implementing the least-squares method in a recursive way. The performance of WDRLS is investigated with real network traffic. Simulation results demonstrate the feasibility of using the linear correlation structure to predict SRD wavelet coefficients and show that WDRLS can achieve high prediction accuracy when working with real LAN or WAN traffic.

Published in:

Global Telecommunications Conference, 2003. GLOBECOM '03. IEEE  (Volume:7 )

Date of Conference:

1-5 Dec. 2003

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.