The objective of this volume is to highlight through a collection of
chap- ters some of the recent research works in applied prob ability,
specifically stochastic modeling and optimization. The volume is
organized loosely into four parts. The first part is a col- lection of
several basic methodologies: singularly perturbed Markov chains (Chapter
1), and related applications in stochastic optimal control (Chapter 2);
stochastic approximation, emphasizing convergence properties (Chapter
3); a performance-potential based approach to Markov decision program-
ming (Chapter 4); and interior-point techniques (homogeneous self-dual
embedding and central path following) applied to stochastic programming
(Chapter 5). The three chapters in the second part are concerned with
queueing the- ory. Chapters 6 and 7 both study processing networks - a
general dass of queueing networks - focusing, respectively, on limit
theorems in the form of strong approximation, and the issue of stability
via connections to re- lated fluid models. The subject of Chapter 8 is
performance asymptotics via large deviations theory, when the input
process to a queueing system exhibits long-range dependence, modeled as
fractional Brownian motion.