TY  - CONF
CY  - Kuala Lumpur
AV  - none
N2  - 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.
N1  - 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
KW  - Bellcore; Block maxima; Domain of attraction; Extreme value theory; Gaussians; Generalized extreme value; Network quality of services; Self-similar; Self-similar input; Shape parameters
KW  -  Forecasting; Quality of service; Signal analysis; Signal processing; Weibull distribution
KW  -  Traffic signals
ID  - scholars601
SP  - 229
TI  - Predicting traffic bursts using extreme value theory
Y1  - 2009///
SN  - 9780769535944
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949974801&doi=10.1109%2fICSAP.2009.52&partnerID=40&md5=1f4d2d487469029d8b00e2f2cbf4736d
A1  - Dahab, A.Y.
A1  - Said, A.M.
A1  - Hasbullah, H.
EP  - 233
ER  -