Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun

Self-similar traffic has an underlying dependence structure which exhibits long-range dependence. This is in contrast to classical traffic models, such as Poisson, which exhibit short-range dependence. Self-similar traffic may also exhibit short-range dependence, but this is on its own insufficie...

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Main Author:	Harun, Hani Hamira
Format:	Thesis
Language:	English
Published:	2006
Subjects:	Electronic computers. Computer science
Online Access:	http://ir.uitm.edu.my/id/eprint/850/ http://ir.uitm.edu.my/id/eprint/850/1/TB_HANI%20HAMIRA%20HARUN%20%20CS%2006_5%20P01.pdf

id	uitm-850
recordtype	eprints
spelling	uitm-8502018-10-30T02:25:56Z http://ir.uitm.edu.my/id/eprint/850/ Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun Harun, Hani Hamira Electronic computers. Computer science Self-similar traffic has an underlying dependence structure which exhibits long-range dependence. This is in contrast to classical traffic models, such as Poisson, which exhibit short-range dependence. Self-similar traffic may also exhibit short-range dependence, but this is on its own insufficient to accurately parametric the traffic. Studying self-similar traffic requires models for analytical work and generators for simulation. Having generating algorithms that close to reflect real traffic is important as they allow us to perform simulations that are similar to the real network traffic. Without this, the results from simulations would not accurately reflect the results that would be expected in the real world. In this project, we have used the Successive random algorithm (SRA). Then, we have decided to use Variance time plot and R/S statistics as our statistical analysis tools. We have test the sample path between 0.5<H<1. After we test on the SRA algorithm, we found that the results are not accurate. But compares to RMD, the SRA samples result be more accurate. In term data generation, SRA be slower than dFGN. COPYRIGHT 2006 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/850/1/TB_HANI%20HAMIRA%20HARUN%20%20CS%2006_5%20P01.pdf Harun, Hani Hamira (2006) Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun. 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 Harun, Hani Hamira Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun
description	Self-similar traffic has an underlying dependence structure which exhibits long-range dependence. This is in contrast to classical traffic models, such as Poisson, which exhibit short-range dependence. Self-similar traffic may also exhibit short-range dependence, but this is on its own insufficient to accurately parametric the traffic. Studying self-similar traffic requires models for analytical work and generators for simulation. Having generating algorithms that close to reflect real traffic is important as they allow us to perform simulations that are similar to the real network traffic. Without this, the results from simulations would not accurately reflect the results that would be expected in the real world. In this project, we have used the Successive random algorithm (SRA). Then, we have decided to use Variance time plot and R/S statistics as our statistical analysis tools. We have test the sample path between 0.5<H<1. After we test on the SRA algorithm, we found that the results are not accurate. But compares to RMD, the SRA samples result be more accurate. In term data generation, SRA be slower than dFGN. COPYRIGHT
format	Thesis
author	Harun, Hani Hamira
author_facet	Harun, Hani Hamira
author_sort	Harun, Hani Hamira
title	Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun
title_short	Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun
title_full	Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun
title_fullStr	Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun
title_full_unstemmed	Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun
title_sort	self-similar network traffic using successive random addition (sra) algorithm / hani hamira harun
publishDate	2006
url	http://ir.uitm.edu.my/id/eprint/850/ http://ir.uitm.edu.my/id/eprint/850/1/TB_HANI%20HAMIRA%20HARUN%20%20CS%2006_5%20P01.pdf
first_indexed	2023-09-18T22:45:07Z
last_indexed	2023-09-18T22:45:07Z
_version_	1777417172629323776

Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun

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