Theses Doctoral

Studies in Stochastic Networks: Efficient Monte-Carlo Methods, Modeling and Asymptotic Analysis

Dong, Jing

This dissertation contains two parts. The first part develops a series of bias reduction techniques for: point processes on stable unbounded regions, steady-state distribution of infinite server queues, steady-state distribution of multi-server loss queues and loss networks and sample path of stochastic differential equations. These techniques can be applied for efficient performance evaluation and optimization of the corresponding stochastic models. We perform detailed running time analysis under heavy traffic of the perfect sampling algorithms for infinite server queues and multi-server loss queues and prove that the algorithms achieve nearly optimal order of complexity. The second part aims to model and analyze the load-dependent slowdown effect in service systems. One important phenomenon we observe in such systems is bi-stability, where the system alternates randomly between two performance regions. We conduct heavy traffic asymptotic analysis of system dynamics and provide operational solutions to avoid the bad performance region.


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More About This Work

Academic Units
Industrial Engineering and Operations Research
Thesis Advisors
Blanchet, Jose H.
Ph.D., Columbia University
Published Here
August 12, 2014