Robust Statistical Modeling Based on Moment Classes, With Applications to Admission Control, Large Deviations and Hypothesis Testing
Pandit, Charuhas Pravin
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https://hdl.handle.net/2142/80883
Description
Title
Robust Statistical Modeling Based on Moment Classes, With Applications to Admission Control, Large Deviations and Hypothesis Testing
Author(s)
Pandit, Charuhas Pravin
Issue Date
2004
Doctoral Committee Chair(s)
Meyn, Sean P.
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
The goal in the admission control problem considered here is to choose a suitable algorithm for admitting or rejecting sources on the basis of on-line measurements of packet statistics, in order to keep a certain overflow probability below a pre-specified threshold. The theory of extremal distributions developed in this thesis is applied to the design of robust algorithms for measurement-based admission control. In addition, models are developed for the evolution of flows and packets in the admission control system, and performance evaluation of the proposed algorithms is carried out through both simulations and analysis. Results show that the robust algorithms minimize the overflow probability among all moment-consistent algorithms.
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