Che Ku Jusoh, Che Ku Noreymie (2006) Generation of self similar network traffic using DFGN algorithm / Che Ku Noreymie Che Ku Jusoh. Degree thesis, Universiti Teknologi MARA.

Text
TB_CHE KU NOREYMIE CHE KU JUSOH CS 06_5 P01.pdf Download (110kB)  Preview 
Abstract
This thesis discusses the generation of network traffic using discrete Fractional Gaussian Noise (dFGN) algorithm. Since the traffic on a number of existing networks is bursty, much research focuses on how to capture the characteristics of traffic to reduce the impact of burstiness. Conventional traffic models do not represent the characteristics of burstiness well, but self–similar traffic models provide a closer approximation. Selfsimilar traffic models have two fundamental properties, long–range dependence and infinite variance, which have been found in a large number of measurement of real traffic. Selfsimilar traffic models also have been found to be more appropriate for the representation of bursty telecommunication traffic. The main starting point for selfsimilar traffic generation is the production of fractional Brownian motion (FBM) or fractional Gaussian noise (FGN). Fractional Brownian motion or Fractional Gaussian Noise is not only of interest for generation of network traffic. Its properties have been investigated by researchers in theoretical physics, probability, statistics, hydrology, biology, and many others. As a result, the techniques that have been used to study this Gaussian process are quite diverse, and it may take some effort to study them. Undoubtedly, this also makes the field more interesting. After generating FBM sample traces, a further transformation needs to be conducted with testing the result to produce the selfsimilar traffic. Testing is done using R/S statistic and Variance Time plot method. After analyzed the result from both tools, the accuracy is more to R/S statistic rather than Variance Time Plot. However, the test result from data 0.5 shows that VT plot is more accurate rather than R/S statistic because the result for VT plot is exactly 0.5. As a conclusion, statistical analysis of the data collected tells us that the selfsimilarity is implementing in the dfgn algorithm.
Item Type:  Thesis (Degree)  

Creators: 


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:  847  
Last Modified:  25 Oct 2018 08:00  
Depositing User:  Staf Pendigitalan 1  
URI:  http://ir.uitm.edu.my/id/eprint/847 
Actions (login required)
View Item 