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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| Format: | Student Project |
| Language: | en |
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Faculty of Information Technology and Quantitative Science
2006
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| Online Access: | https://ir.uitm.edu.my/id/eprint/853/1/PPb_JUMALIAH%20SAARINI%20CS%2006_5%20P01.pdf https://ir.uitm.edu.my/id/eprint/853/ |
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| _version_ | 1833057973448998912 |
|---|---|
| author | Saarini, Jumaliah |
| author_facet | Saarini, Jumaliah |
| author_sort | Saarini, Jumaliah |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uitm.ir-853 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2006 |
| publisher | Faculty of Information Technology and Quantitative Science |
| record_format | eprints |
| spelling | my.uitm.ir-8532018-10-30T03:36:14Z https://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 https://ir.uitm.edu.my/id/eprint/853/1/PPb_JUMALIAH%20SAARINI%20CS%2006_5%20P01.pdf Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini. (2006) [Student Project] (Unpublished) |
| 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 |
| title | 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_short | 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 |
| topic | Electronic Computers. Computer Science Computer networks. General works. Traffic monitoring |
| url | https://ir.uitm.edu.my/id/eprint/853/1/PPb_JUMALIAH%20SAARINI%20CS%2006_5%20P01.pdf https://ir.uitm.edu.my/id/eprint/853/ |
| url_provider | http://ir.uitm.edu.my/ |