The proliferation of local and global communication networks for computer and human communication, the development of parallel computers with large numbers of processors and the design of flexible and robust manufacturing systems, have increased the understanding of queuing theory. While research on queuing networks uses many of the traditional queuing insights, it is more concerned with how network components interact than with detailed models of how an individual queue behaves. Topics discussed in this book include: the conditions for stability of multiclass queuing networks; the challenges reflected in the Brownian motion which is set both as a mathematical object and as a modelling paradigm; the usefulness of ideas from the interacting particle system world; the application of large deviation theory; and the developing connections with optimization and dynamical systems theory.
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The theory of stochastic networks is an important and rapidly developing research area, driven in part by important industrial applications in the design and control of modern communications and manufacturing networks. This volume is a collections of invited papers written by some of the leading researchers in this field, and provides a comprehensive survey of current research and the very latest developments. With contributions from most of the world's foremost researchers the areas covered include the mathematical modelling and the optimal control of queueing and loss networks, and the statistical modelling and analysis of network data. Also containing a comprehensive and up-to-date bibliography of the statistical literature on long-range dependence and self-similarity in network traffic and other scientific and engineering applications this book will suit researchers, research institutes and industry throughout the world.
Frank Kelly is Professor of the Mathematics of Systems at the University of Cambridge. His main research interests are in random processes, networks and optimization. He is especially interested in applications to the design and control of networks and to the understanding of self-regulation in large-scale systems.
Elena Yudovina is a postdoctoral research fellow at the University of Michigan. Her research interests are in applications of queueing theory. She received her PhD from the University of Cambridge, where her interest in the subject was sparked by a course on stochastic networks taught by Frank Kelly.
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