Predicting Traffic Bursts Using Extreme Value Theory
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2009
|
| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/4585/1/05163861.pdf http://www.computer.org/portal/web/csdl/doi/10.1109/ICSAP.2009.52 http://eprints.utp.edu.my/4585/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|