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

Intelligent IP traffic matrix estimation by neural network and genetic algorithm

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

2 Author(s)
Omidvar, A. ; Electr. Eng. Dept., Iran Univ. of Sci. & Technol., Tehran, Iran ; Shahhoseini, H.S.

Rapid growth of computer network scales has made traffic matrix estimation essential in network management. It can be used in load balancing, traffic detecting and so on. Since traffic should be considered temporally and spatially, prediction is complicated. Tracking dynamic changes of traffic, reducing estimation errors and increasing robustness to noise are factors which should be considered in estimation. In this paper, we propose a novel method to estimate traffic matrix. This approach combines artificial neural network and evolutionary algorithms. It uses autoregressive model with exogenous inputs (ARX) joined with genetic algorithm (GA) which we call it ARXGEN. GA is used in gaining optimized weights and biases. To evaluate our method, we did our simulations on Abilene data. Results prove that it can well track dynamic nature of traffic and has lower estimation errors. It is also more robust to noise.

Published in:

Intelligent Signal Processing (WISP), 2011 IEEE 7th International Symposium on

Date of Conference:

19-21 Sept. 2011

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.