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. Convention...
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myuitmir.84720181025T08:00:41Z Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh 2006 Che Ku Jusoh, Che Ku Noreymie Electronic Computers. Computer Science 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. Selfsimilar traffic models also have been found to be more appropriate for the representation of bursty telecommunication traffic. The main starting point for selfsimilar 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 selfsimilar 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. 2006 Thesis https://ir.uitm.edu.my/id/eprint/847/ https://ir.uitm.edu.my/id/eprint/847/1/TB_CHE%20KU%20NOREYMIE%20CHE%20KU%20JUSOH%20CS%2006_5%20P01.pdf text en public degree Universiti Teknologi MARA Perpustakaan Tun Abdul Razak 
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Universiti Teknologi MARA 
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English 
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Electronic Computers Computer Science 
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Electronic Computers Computer Science Che Ku Jusoh, Che Ku Noreymie Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh 
description 
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. Selfsimilar traffic models also have been found to be more appropriate for the
representation of bursty telecommunication traffic.
The main starting point for selfsimilar 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 selfsimilar 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. 
format 
Thesis 
qualification_level 
Bachelor degree 
author 
Che Ku Jusoh, Che Ku Noreymie 
author_facet 
Che Ku Jusoh, Che Ku Noreymie 
author_sort 
Che Ku Jusoh, Che Ku Noreymie 
title 
Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh 
title_short 
Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh 
title_full 
Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh 
title_fullStr 
Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh 
title_full_unstemmed 
Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh 
title_sort 
generation of self similar network traffic using dfgn algorithm / che ku noreymie che ku jusoh 
granting_institution 
Universiti Teknologi MARA 
granting_department 
Perpustakaan Tun Abdul Razak 
publishDate 
2006 
url 
https://ir.uitm.edu.my/id/eprint/847/1/TB_CHE%20KU%20NOREYMIE%20CHE%20KU%20JUSOH%20CS%2006_5%20P01.pdf 
_version_ 
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