%A A.Y. Dahab
%A A.M. Said
%A H. Hasbullah
%T Predicting traffic bursts using extreme value theory
%C Kuala Lumpur
%P 229-233
%K Bellcore; Block maxima; Domain of attraction; Extreme value theory; Gaussians; Generalized extreme value; Network quality of services; Self-similar; Self-similar input; Shape parameters, Forecasting; Quality of service; Signal analysis; Signal processing; Weibull distribution, Traffic signals
%X Traffic Bursts appear to be more pronounced recently and have major consequences for network Quality of Service. We investigate the extreme behavior of bursts and quantify the probabilities of these large bursts. Taking Bellcore internal Ethernet traces as an example, we applied Generalized Extreme Value model over block maxima. The analysis reveals that traffic burst maxima follows GEV model with negative shape parameter. Traffic bursts are in the domain of attraction of Weibull distribution. Our result confirms the conclusion of Norros of storage fed with Gaussian self-similar input. © 2009 IEEE.
%L scholars601
%J 2009 International Conference on Signal Acquisition and Processing, ICSAP 2009
%O cited By 4; Conference of 2009 International Conference on Signal Acquisition and Processing, ICSAP 2009 ; Conference Date: 3 April 2009 Through 5 April 2009; Conference Code:79615
%R 10.1109/ICSAP.2009.52
%D 2009