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Congestion detection within multi-service TCP/IP networks using wavelets

Using passive observation within the multi-service TCP/IP networking domain, we have developed a methodology that associates the frequency composition of composite traffic signals with the packet transmission mechanisms of TCP. At the core of our design is the Discrete Wavelet Transform (DWT), used to temporally localise the frequency variations of a signal. Our design exploits transmission mechanisms (including Fast Retransmit/Fast Recovery, Congestion Avoidance, Slow start, and Retransmission Timer Expiry with Exponential Back off.) that are activated in response to changes within this type of network environment. Manipulation of DWT output, combined with the use of novel heuristics permits shifts in the frequency spectrum of composite traffic signals to be directly associated with the former. Our methodology can be adapted to accommodate composite traffic signals that contain a substantial proportion of data originating from non-rate adaptive sources often associated with Long Range Dependence and Self Similarity (e.g. Pareto sources). We demonstrate the methodology in two ways. Firstly, it is used to design a congestion indicator tool that can operate with network control mechanisms that dissipate congestion. Secondly, using a queue management algorithm (Random Early Detection) as a candidate protocol, we show how our methodology can be adapted to produce a performance-monitoring tool. Our approach provides a solution that has both low operational and implementation intrusiveness with respect to existing network infrastructure. The methodology requires a single parameter (i.e. the arrival rate of traffic at a network node), which can be extracted from almost all network-forwarding devices. This simplifies implementation. Our study was performed within the context of fault management with design requirements and constraints arising from an in depth study of the Fault Management Systems (FMS) used by British Telecomm on regional UK networks up to February 2000.
Date January 2004
CreatorsJarrett, Wayne O'Brian
PublisherUniversity College London (University of London)
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation

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