# Sidney Resnick, Cornell University

Event Details
• Date/Time:
• Tuesday August 25, 2009 - Wednesday August 26, 2009
11:00 am - 11:59 am
• Location: Executive classroom
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• Fee(s):
N/A
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Contact

Ton Dieker, ISyE
Contact Ton Dieker
404-385-3140

Summaries

Summary Sentence: Modeling Data Network Sessions by the Conditional Extreme Value Model

Full Summary: 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 Lopez-Oliveros.)

Speaker
Sidney Resnick
Cornell University

Abstract
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 Lopez-Oliveros.)

Bio
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.

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School of Industrial and Systems Engineering (ISYE)

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Status
• Created By: Mike Alberghini
• Workflow Status: Published
• Created On: Dec 20, 2012 - 11:13am
• Last Updated: Oct 7, 2016 - 10:01pm