By Topic

A Recurrent Neural Network approach to traffic matrix tracking using partial measurements

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)
Feng Qian ; Key Lab. of Broadband Opt. Fiber Transm. & Commun. Networks, Univ. of Electron. Sci. & Technol. of China (UESTC), Chengdu ; Guangmin Hu ; Jijun Xie

Traffic matrix allows network engineers and managers to solve problems in design, routing, configuration debugging, monitoring and pricing. Direct measurement of traffic matrix is usually not implemented because it is too expensive. Instead, we can easily measure the loads on every link and inference traffic matrix by using network tomography technology. In this paper, we develop a novel network tomography approach using recurrent neural network (RNN) that track origin-destination traffic matrix based on partial measurements without any prior information. Our RNN approach not only allows us to estimate traffic matrix and can also be used to predict traffic. Using real data collected from a Ailebant network, we illustrate that our proposed approach can achieve lower errors than general Gravity model prior.

Published in:

Industrial Electronics and Applications, 2008. ICIEA 2008. 3rd IEEE Conference on

Date of Conference:

3-5 June 2008