Robust Statistical Modeling Based on Moment Classes, With Applications to Admission Control, Large Deviations and Hypothesis Testing

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Pandit, Charuhas Pravin

Contributors dc:contributor

• Meyn, Sean P.

• Engineering, Electronics and Electrical

Language dc:language

eng

Identifier

(MiAaPQ)AAI3153394

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/80883