D.Ing. / Conventional traffic models used for the analysis of packet-switched data are Markovian in nature and are based on assumptions, such as Poissonian arrivals. The introduction of packet oriented networks has resulted in an influx of information highlighting numerous discrepancies from these assumptions. Several studies have shown that traffic patterns from diverse packet-switched networks and services exhibit the presence of properties such as self-similarity, long-range dependencies, slowly decaying variances, "heavy tailed" or power law distributions, and fractal structures. Heavy Tailed distributions decay slower than predicted by conventional exponential assumptions and lead to significant underestimation of network traffic variables. Furthermore, it was shown that the statistical multiplexing of multiple packet-switched sources do not give rise to a more homogenous aggregate, but that properties such as burstiness are conserved. The results of the above mentioned studies have shown that none of the commonly used traffic models and assumptions are able to completely capture the bursty behaviour of packet- and cellbased networks. Artificial Intelligent methods provide the capability to extract the inherent characteristics of a system and include soft decision-making approaches such as Fuzzy Logic. Adaptive methods such as Fuzzy Logic Self-learning algorithms have the potential to solve some of the most pressing problems of traffic Modelling and Management in modern packet-switched networks. This dissertation is concerned with providing alternative solutions to the mentioned problems, in the following three sub-sections; the Description of Heavy Tailed Arrival Distributions, Timeseries Forecasting of bursty Traffic Intensities, and Management related Soft Decision-Making. Although several alternative methods, such as Kalman Filters, Bayesian Distributions, Fractal Analysis and Neural Networks are considered, the main emphasis of this work is on Fuzzy Logic applications.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:9959 |
| Date | 11 September 2012 |
| Creators | Scheffer, Marten F. |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
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