Traffic Matrix Estimation Using The Levenberg-
Marquardt Neural Network Of A Large IP System
Author(s)
S. Mekaoui1, C. Benhamed1 & K. Ghoumid
Abstract
This paper deals with a method using a specific class of neural networks whose
learning phase is based on the Levenberg-Marquardt algorithm and which had
been applied to the estimation of the traffic matrix (TM) of a large scale IP
network. The neural network had been implemented with the help of the specific
neural toolbox of the source software Matlab. Such neural networks are within
the class of feed forward and recurrent types. The simulation tests have been
processed on the available data base of the very reputed American observatory
data base on the Internet of a very large scale IP network, the so-called Abilene
network on both categories of neural networks. The simulated results using this
method have been found to be very accurate as compared to one another. The
static model converges rapidly but was less accurate in the estimation of the
Traffic Matrix of such a kind of large IP System (the Abilene System) than the
dynamic model which in this way earned the challenge of yielding a perfect
estimation.
Keywords: IP networks, Traffic Matrix (TM), neural networks (NN), Levenberg-
Marquardt learning algorithm, Traffic Matrix estimation, linear regression.
Keywords
Keywords: IP networks, Traffic Matrix (TM), neural networks (NN), Levenberg-
Marquardt learning algorithm, Traffic Matrix estimation, linear regression.
Related Book
Other papers in this volume
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