Performance Modeling, Stochastic Networks, and Statistical Multiplexing, Second Edition

Ravi R. Mazumdar, Ravi Mazumdar

• Publisher: Springer
• Year: 2022
• Print ISBN: 9783031792595
• eBook ISBN: 9783031792601
• Edition: 2
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in computing performance measures. The monograph also covers stochastic network theory including Markovian networks. Recent results on network utility optimization and connections to stochastic insensitivity are discussed. Also presented are ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. In particular, the important concept of effective bandwidths as mappings from queueing level phenomena to loss network models is clearly presented along with a detailed discussion of accurate approximations for large networks.

Table of contents

• Cover
• Copyright Page
• Title Page
• Dedication
• Contents
• Preface
• 1 Introduction toTraffic Models and Analysis
• 2 Queues and Performance Analysis
• 3 Loss Models forNetworks
• 4 StochasticNetworks and Insensitivity
• 5 Statistical Multiplexing
• A Review of Probability and Markov Chains
• Bibliography
• Author’s Biography
• Index