Abstract
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.