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Towards automatic traffic classification and estimation for available bandwidth in IP networks.

Lai, Z.; (2008) Towards automatic traffic classification and estimation for available bandwidth in IP networks. Doctoral thesis , University of London. Green open access

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Abstract

Growing rapidly, today's Internet is becoming more difficult to manage. A good understanding of what kind of network traffic classes are consuming network resource as well as how much network resource is available is important for many management tasks like QoS provisioning and traffic engineering. In the light of these objectives, two measurement mechanisms have been explored in this thesis. This thesis explores a new type of traffic classification scheme with automatic and accurate identification capability. First of all, the novel concept of IP flow profile, a unique identifier to the associated traffic class, has been proposed and the relevant model using five IP header based contexts has been presented. Then, this thesis shows that the key statistical features of each context, in the IP flow profile, follows a Gaussian distribution and explores how to use Kohonen Neural Network (KNN) for the purpose of automatically producing IP flow profile map. In order to improve the classification accuracy, this thesis investigates and evaluates the use of PCA for feature selection, which enables the produced patterns to be as tight as possible since tight patterns lead to less overlaps among patterns. In addition, the use of Linear Discriminant Analysis and alternative KNN maps has been investigated as to deal with the overlap issue between produced patterns. The entirety of this process represents a novel addition to the quest for automatic traffic classification in IP networks. This thesis also develops a fast available bandwidth measurement scheme. It firstly addresses the dynamic problem for the one way delay (OWD) trend detection. To deal with this issue, a novel model - asymptotic OWD Comparison (AOC) model for the OWD trend detection has been proposed. Then, three statistical metrics SOT (Sum of Trend), PTC (Positive Trend Checking) and CTC (Complete Trend Comparison) have been proposed to develop the AOC algorithms. To validate the proposed AOC model, an avail-bw estimation tool called Pathpair has been developed and evaluated in the Planetlah environment.

Type: Thesis (Doctoral)
Title: Towards automatic traffic classification and estimation for available bandwidth in IP networks.
Identifier: PQ ETD:591522
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Thesis digitised by ProQuest. Third party copyright material has been removed from the ethesis
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
URI: https://discovery.ucl.ac.uk/id/eprint/1444220
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