Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh

This thesis discusses the generation of network traffic using discrete Fractional
Gaussian Noise (dFGN) algorithm. Since the traffic on a number of existing networks is
bursty, much research focuses on how to capture the characteristics of traffic to reduce
the impact of burstiness. Conventional traffic models do not represent the characteristics
of burstiness well, but self–similar traffic models provide a closer approximation. Selfsimilar traffic models have two fundamental properties, long–range dependence and
infinite variance, which have been found in a large number of measurement of real
traffic. Self-similar traffic models also have been found to be more appropriate for the
representation of bursty telecommunication traffic.
The main starting point for self-similar traffic generation is the production of
fractional Brownian motion (FBM) or fractional Gaussian noise (FGN). Fractional
Brownian motion or Fractional Gaussian Noise is not only of interest for generation of
network traffic. Its properties have been investigated by researchers in theoretical
physics, probability, statistics, hydrology, biology, and many others. As a result, the
techniques that have been used to study this Gaussian process are quite diverse, and it
may take some effort to study them. Undoubtedly, this also makes the field more
interesting.
After generating FBM sample traces, a further transformation needs to be
conducted with testing the result to produce the self-similar traffic. Testing is done using
R/S statistic and Variance Time plot method. After analyzed the result from both tools,
the accuracy is more to R/S statistic rather than Variance Time Plot. However, the test
result from data 0.5 shows that VT plot is more accurate rather than R/S statistic because
the result for VT plot is exactly 0.5.
As a conclusion, statistical analysis of the data collected tells us that the selfsimilarity is implementing in the dfgn algorithm.