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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Main Author:	Che Ku Jusoh, Che Ku Noreymie
Format:	Thesis
Language:	English
Published:	2006
Subjects:	Electronic computers. Computer science
Online Access:	http://ir.uitm.edu.my/id/eprint/847/ http://ir.uitm.edu.my/id/eprint/847/1/TB_CHE%20KU%20NOREYMIE%20CHE%20KU%20JUSOH%20CS%2006_5%20P01.pdf

id	uitm-847
recordtype	eprints
spelling	uitm-8472018-10-25T08:00:41Z http://ir.uitm.edu.my/id/eprint/847/ Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh 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. 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. 2006 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/847/1/TB_CHE%20KU%20NOREYMIE%20CHE%20KU%20JUSOH%20CS%2006_5%20P01.pdf Che Ku Jusoh, Che Ku Noreymie (2006) Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh. Degree thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Electronic computers. Computer science
spellingShingle	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. 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.
format	Thesis
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
publishDate	2006
url	http://ir.uitm.edu.my/id/eprint/847/ http://ir.uitm.edu.my/id/eprint/847/1/TB_CHE%20KU%20NOREYMIE%20CHE%20KU%20JUSOH%20CS%2006_5%20P01.pdf
first_indexed	2023-09-18T22:45:06Z
last_indexed	2023-09-18T22:45:06Z
_version_	1777417172196261888

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

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