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Performance Modeling, Stochastic Networks, and Statistical Multiplexing, Second Edition [electronic resource] / by Ravi R. Mazumdar.

By: Mazumdar, Ravi R [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Learning, Networks, and Algorithms: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2013Edition: 2nd ed. 2013.Description: XIV, 197 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031792601.Subject(s): Artificial intelligence | Cooperating objects (Computer systems) | Programming languages (Electronic computers) | Telecommunication | Artificial Intelligence | Cyber-Physical Systems | Programming Language | Communications Engineering, NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction to Traffic Models and Analysis -- Queues and Performance Analysis -- Loss Models for Networks -- Stochastic Networks and Insensitivity -- Statistical Multiplexing.
In: Springer Nature eBookSummary: 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.
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Introduction to Traffic Models and Analysis -- Queues and Performance Analysis -- Loss Models for Networks -- Stochastic Networks and Insensitivity -- Statistical Multiplexing.

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.

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