Modeling Data Network Sessions by the Conditional Extreme Value Model

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TITLE: Modeling Data Network Sessions by the Conditional Extreme Value Model

SPEAKER: Sidney Resnick


Using various rules, the flow of packets past a sensor can be amalgamated into higher level entities called sessions. Statistical analysis of these sessions is complex: session duration (D) and size (S) are jointly heavy tailed but average transmission rate (R=S/D) is not heavy tailed and
arrival times of sessions is not Poisson. By segmenting sessions using a peak rate covariate, we find conditional on segment that within segment session initiations can be modeled as Poisson. For modeling the distribution of (D,S,R), the conditional extreme value (CEV) model is
useful. This model is an alternative to multivariate extreme value modeling and is applicable to modeling the distribution of a random vector if some component of the vector is not in a unidimensional domain of attraction. Combining these elements, an overall model of packet flows
emerges which is suitable for simulation.
(Joint work at various times with Jan Heffernan, Bikramjit Das, Luis

Dr. Sid Resnick received Masters and PhD. degrees from Purdue University
in 1968 and 1970, respectively and has since worked at the Technion
(Haifa), Stanford University, Colorado State University and for the last
20 years at Cornell University. He has authored 4 books and coauthored 151
journal articles appearing in major international journals. Sid is also a
fellow of the Institute of Mathematical Statistics. His research and
scholarship areas cover heavy tails, statistical analysis of
extremes, service systems and networks.


  • Workflow Status: Published
  • Created By: Anita Race
  • Created: 10/12/2009
  • Modified By: Fletcher Moore
  • Modified: 10/07/2016


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