*Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun.*Degree thesis, Universiti Teknologi MARA.

## Abstract

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.

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## Metadata

Item Type: | Thesis (Degree) |
---|---|

Creators: | Creators Email / ID Num. Harun, Hani Hamira UNSPECIFIED |

Subjects: | Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science |

Divisions: | Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences |

Date: | 2006 |

URI: | https://ir.uitm.edu.my/id/eprint/850 |

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