By Topic

Neural networks for the adaptive control of disruptive nonlinear 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 $31
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)
Greenwood, D.P.A. ; Fujitsu Lab. of America Inc., Sunnyvale, CA, USA ; Carrasco, R.A.

A study has been made of nonlinear behaviour found within the traffic profile and physical components of distributed communications networks, with particular attention given to the chaotic dynamics produced at the extremes of such behaviour. This analysis has led to the development of an algorithm describing how an arbitrary traffic flow may be monitored and characterised by a discrete sequence of analytical and statistical metrics. These metrics are then employed by a network-wide distributed flow control mechanism based on adaptive traffic routing, using a neural processor. The processor is configured to retain an imprint of recent flow characterisation to build a projection of likely future behaviour. Results show that when applied to networks operating across a wide variance of traffic conditions, the flow management system improves end-to-end delay through maintaining and/or regaining traffic flow stability

Published in:

Communications, IEE Proceedings-  (Volume:147 ,  Issue: 5 )