*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
TITLE: MODELING DATA NETWORK SESSIONS BY THE CONDITIONAL EXTREME VALUE MODEL
SPEAKER: Professor 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.
