Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini

This project is to generate the self-similar network traffic. It is generally accepted that self-similar or fractal process may provide better models for in modern network traffic than Poisson process. Poisson arrival processes are not self-similar, regardless of degree of aggregation. The way to...

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Main Author:	Saarini, Jumaliah
Format:	Student Project
Language:	English
Published:	Faculty of Information Technology and Quantitative Science 2006
Subjects:	Electronic computers. Computer science Computer networks. General works. Traffic monitoring
Online Access:	http://ir.uitm.edu.my/id/eprint/853/ http://ir.uitm.edu.my/id/eprint/853/1/PPb_JUMALIAH%20SAARINI%20CS%2006_5%20P01.pdf

id	uitm-853
recordtype	eprints
spelling	uitm-8532018-10-30T03:36:14Z http://ir.uitm.edu.my/id/eprint/853/ Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini Saarini, Jumaliah Electronic computers. Computer science Computer networks. General works. Traffic monitoring This project is to generate the self-similar network traffic. It is generally accepted that self-similar or fractal process may provide better models for in modern network traffic than Poisson process. Poisson arrival processes are not self-similar, regardless of degree of aggregation. The way to solve this problem, we applied the existed method in visual C++ programming with used the Random midpoint Displacement (RMD) algorithm. That program we need the sequence of the random number as a data. The data was generated depends on the power of two of data. The numbers of data will be analyzed using the R/S Statistic program and Variance Time Plot program. That analysis programs were running in MathCAD v12 platform. The graft will be display after the data is running in the analysis programs as result. The new values of Hurst will be appear as a results whether the self-similar or not. After the analysis process, the result from the R/S Statistic and Variance Tome Plot were not accurate. The new value of Hurst was not exactly same with the expected value of Hurst. As a conclusion, using RMD algorithm the result are more satisfy compare using the traditional process because the result are more accurate are more faster. The RMD fastest in term of computational time but do not accurately reflect the Hurst parameter. Faculty of Information Technology and Quantitative Science 2006 Student Project NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/853/1/PPb_JUMALIAH%20SAARINI%20CS%2006_5%20P01.pdf Saarini, Jumaliah (2006) Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini. [Student Project] (Unpublished)
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 Computer networks. General works. Traffic monitoring
spellingShingle	Electronic computers. Computer science Computer networks. General works. Traffic monitoring Saarini, Jumaliah Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini
description	This project is to generate the self-similar network traffic. It is generally accepted that self-similar or fractal process may provide better models for in modern network traffic than Poisson process. Poisson arrival processes are not self-similar, regardless of degree of aggregation. The way to solve this problem, we applied the existed method in visual C++ programming with used the Random midpoint Displacement (RMD) algorithm. That program we need the sequence of the random number as a data. The data was generated depends on the power of two of data. The numbers of data will be analyzed using the R/S Statistic program and Variance Time Plot program. That analysis programs were running in MathCAD v12 platform. The graft will be display after the data is running in the analysis programs as result. The new values of Hurst will be appear as a results whether the self-similar or not. After the analysis process, the result from the R/S Statistic and Variance Tome Plot were not accurate. The new value of Hurst was not exactly same with the expected value of Hurst. As a conclusion, using RMD algorithm the result are more satisfy compare using the traditional process because the result are more accurate are more faster. The RMD fastest in term of computational time but do not accurately reflect the Hurst parameter.
format	Student Project
author	Saarini, Jumaliah
author_facet	Saarini, Jumaliah
author_sort	Saarini, Jumaliah
title	Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini
title_short	Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini
title_full	Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini
title_fullStr	Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini
title_full_unstemmed	Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini
title_sort	self-similar network traffic using random midpoint displacement (rmd) algorithm / jumaliah saarini
publisher	Faculty of Information Technology and Quantitative Science
publishDate	2006
url	http://ir.uitm.edu.my/id/eprint/853/ http://ir.uitm.edu.my/id/eprint/853/1/PPb_JUMALIAH%20SAARINI%20CS%2006_5%20P01.pdf
first_indexed	2023-09-18T22:45:07Z
last_indexed	2023-09-18T22:45:07Z
_version_	1777417173056094208

Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini

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