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Application Of Extreme Value Theory To Bursts Prediction
Abdelmahamoud Youssouf Dahab, Abas bin Md Said, Halabi bin Hasbullah
Pages - 55 - 63     |    Revised - 30-09-2009     |    Published - 21-10-2009
Volume - 3   Issue - 4    |    Publication Date - August 2009  Table of Contents
Bursts, Extreme Value Theory, Prediction, Quality of Service
Bursts and extreme events in quantities such as connection durations, file sizes, throughput, etc. may produce undesirable consequences in computer networks. Deterioration in the quality of service is a major consequence. Predicting these extreme events and burst is important. It helps in reserving the right resources for a better quality of service. We applied Extreme value theory (EVT) to predict bursts in network traffic. We took a deeper look into the application of EVT by using EVT based Exploratory Data Analysis. We found that traffic is naturally divided into two categories, Internal and external traffic. The internal traffic follows generalized extreme value (GEV) model with a negative shape parameter, which is also the same as Weibull distribution. The external traffic follows a GEV with positive shape parameter, which is Frechet distribution. These findings are of great value to the quality of service in data networks, especially when included in service level agreement as traffic descriptor parameters.
CITED BY (5)  
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Mr. Abdelmahamoud Youssouf Dahab
- Malaysia
Dr. Abas bin Md Said
- Malaysia
Dr. Halabi bin Hasbullah
- Malaysia