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Title:Robust Statistical Modeling Based on Moment Classes, With Applications to Admission Control, Large Deviations and Hypothesis Testing
Author(s):Pandit, Charuhas Pravin
Doctoral Committee Chair(s):Meyn, Sean P.
Department / Program:Electrical Engineering
Discipline:Electrical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Engineering, Electronics and Electrical
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.
Issue Date:2004
Description:104 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.
Other Identifier(s):(MiAaPQ)AAI3153394
Date Available in IDEALS:2015-09-25
Date Deposited:2004

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