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Performance Analysis of Queuing and Computer Networks
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Performance Analysis of Queuing and Computer Networks

Book Details

Format Paperback / Softback
ISBN-10 0367387220
ISBN-13 9780367387228
Publisher Taylor & Francis Ltd
Imprint Chapman & Hall/CRC
Country of Manufacture GB
Country of Publication GB
Publication Date Sep 19th, 2019
Print length 472 Pages
Weight 712 grams
Dimensions 15.70 x 23.50 x 2.80 cms
Ksh 12,000.00
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With examples and exercises, this text develops simple models and analyticalmethods from first principles to evaluate performance metrics of various configurations of computer systems and networks. It includes simple, analytically tractable models, such as optimization of saturated CSMA/CD and CSMA/CA LANs; presents models for complex systems as analyzable modifications and/or interconnections of simple models; and contains a wide variety of queuing models, including bursty, MMPP, approximate self-similar traffic, and fluid flow. The author also discusses timing and synchronization in the analysis of discrete time (slotted) networks.
Performance Analysis of Queuing and Computer Networks develops simple models and analytical methods from first principles to evaluate performance metrics of various configurations of computer systems and networks. It presents many concepts and results of probability theory and stochastic processes.

After an introduction to queues in computer networks, this self-contained book covers important random variables, such as Pareto and Poisson, that constitute models for arrival and service disciplines. It then deals with the equilibrium M/M/1/8queue, which is the simplest queue that is amenable for analysis. Subsequent chapters explore applications of continuous time, state-dependent single Markovian queues, the M/G/1 system, and discrete time queues in computer networks. The author then proceeds to study networks of queues with exponential servers and Poisson external arrivals as well as the G/M/1 queue and Pareto interarrival times in a G/M/1 queue. The last two chapters analyze bursty, self-similar traffic, and fluid flow models and their effects on queues.


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