Get Queueing Networks and Markov Chains: Modeling and PDF

By Gunter Bolch, Stefan Greiner, Hermann de Meer, Kishor S. Trivedi(auth.)

ISBN-10: 0471565253

ISBN-13: 9780471565253

ISBN-10: 0471791571

ISBN-13: 9780471791577

Seriously acclaimed textual content for desktop functionality analysis--now in its moment edition

the second one variation of this now-classic textual content presents a present and thorough therapy of queueing structures, queueing networks, non-stop and discrete-time Markov chains, and simulation. completely up-to-date with new content material, in addition to new difficulties and labored examples, the textual content deals readers either the idea and useful counsel had to behavior functionality and reliability reviews of laptop, verbal exchange, and production systems.

beginning with easy chance idea, the textual content units the basis for the extra advanced subject matters of queueing networks and Markov chains, utilizing functions and examples to demonstrate key issues. Designed to have interaction the reader and construct sensible functionality research talents, the textual content incorporates a wealth of difficulties that replicate real challenges.

New beneficial properties of the second one variation include:
* bankruptcy analyzing simulation equipment and applications
* functionality research purposes for instant, web, J2EE, and Kanban systems
* most up-to-date fabric on non-Markovian and fluid stochastic Petri nets, in addition to resolution strategies for Markov regenerative processes
* up-to-date discussions of latest and well known functionality research instruments, together with ns-2 and OPNET
* New and present real-world examples, together with DiffServ routers within the net and mobile cellular networks

With the quickly growing to be complexity of machine and conversation structures, the necessity for this article, which expertly mixes thought and perform, is super. Graduate and complex undergraduate scholars in desktop technology will locate the wide use of examples and difficulties to be important in getting to know either the fundamentals and the tremendous issues of the sector, whereas execs will locate the textual content crucial for constructing structures that agree to criteria and laws.

also, an answer guide and an FTP web site with hyperlinks to author-provided info for the publication can be found for deeper study.Content:
Chapter 1 creation (pages 1–49):
Chapter 2 Markov Chains (pages 51–122):
Chapter three Steady?State answer of Markov Chains (pages 123–184):
Chapter four Steady?State Aggregation/Disaggregation tools (pages 185–208):
Chapter five temporary answer of Markov Chains (pages 209–239):
Chapter 6 unmarried Station Queueing structures (pages 241–319):
Chapter 7 Queueing Networks (pages 321–367):
Chapter eight Algorithms for Product?Form Networks (pages 369–420):
Chapter nine Approximation Algorithms for Product?Form Networks (pages 421–460):
Chapter 10 Algorithms for Non?Product?Form Networks (pages 461–605):
Chapter eleven Discrete occasion Simulation (pages 607–656):
Chapter 12 functionality research instruments (pages 657–701):
Chapter thirteen functions (pages 703–806):

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Extra resources for Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications, Second Edition

Sample text

9 where n parallel levels are depicted and each level j contains a series of rj phases connected, each with exponentially distributed time and rate r j p j . Each level j is selected with probability j . The pdf is given by Another type of generalized Erlang distribution can be obtained by lifting the restriction that all the phases within the same level have the same rate, or, alternatively, if there are nonzero exit probabilities assigned such that remaining phases can be skipped. These generalizations lead to very complicated equations that are not further described here.

83) where P ( T > t n - l i a / 2 ) = a/2. If a maximum likelihood estimation is used for the point estimation it is convenient to use likelihood ratio confidence bounds as they can directly reuse the result of the point estimation. These are defined as [MeEs98] L(8) >e-;. 84) L(8) - that was found by the point estimation, xi,, is the X2-quantile with k degrees of freedom, which is just the number of estimated parameters, and the error-probability a. An easy interpretation of Eq. 85) / by taking the natural logarithm ( g ( 8 ) is referred to as confidence function in the remainder of this section).

1 %-Transform Let X be a discrete random variable with the probability mass function pk = P ( X = k ) for k = 0 , 1 , 2 , .. , then thc z-transform of X is defined by The function G,(z) is also called the probability generating function. 4 specifies the probability generating functions of some important discrete random variables. 62) with k 3 a k ~= (-1) ( k - I ) ! , k(k - 1) ak,k-l = 2 ’ ~ k = 3 , 4 , . , k = 3 , 4 , .. , k - 2 . ) Parameter n ,P Binomial -P) +P4" Geometric P P. 2 Laplace Transform The Laplace transform plays a similar role for non-negative continuous random variables as the z-transform does for discrete random variables.

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Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications, Second Edition by Gunter Bolch, Stefan Greiner, Hermann de Meer, Kishor S. Trivedi(auth.)


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