By Topic

Classifying daily patterns in long duration network traces

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
$33 $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)
Brendon Jones ; WAND Network Research Group, Department of Computer Science, University of Waikato, New Zealand ; Richard Nelson

Models of network traffic for use in simulation should be representative of the traffic observed on the type of day they are trying to replicate. Building a model from a single day or small number of days makes it prone to overfitting or being unduly influenced by unusual events. With very long duration traces such as the multiple-year spanning Waikato datasets captured by the WAND Network Research Group it is possible to more accurately characterise behaviour and define appropriate boundaries for when traffic is similar enough and when it is different. We present here an approach to identifying and describing discrete ldquotypesrdquo of days within these traces and what differences are important to distinguish between them. By applying machine learning techniques to the long duration traces it is possible to describe and simulate a generic day of a specific type without it being explicitly based on a particular day. The resulting parameters are used to configure a number of popular traffic generators which are then evaluated using the same criteria with which the model was built.

Published in:

Telecommunication Networks and Applications Conference, 2007. ATNAC 2007. Australasian

Date of Conference:

2-5 Dec. 2007