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

Harun, Hani Hamira (2006) Self-similar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun. Degree thesis, Universiti Teknologi MARA.

[img]
Preview
Text
TB_HANI HAMIRA HARUN CS 06_5 P01.pdf

Download (103kB) | Preview

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. COPYRIGHT

Item Type: Thesis (Degree)
Creators:
CreatorsEmail
Harun, Hani HamiraUNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic computers. Computer science
Q Science > QA Mathematics > Instruments and machines > Electronic computers. Computer science
Divisions: Faculty of Computer and Mathematical Sciences
Item ID: 850
Last Modified: 30 Oct 2018 02:25
Depositing User: Staf Pendigitalan 1
URI: http://ir.uitm.edu.my/id/eprint/850

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year