By Gunter Bolch, Stefan Greiner, Hermann de Meer, Kishor S. Trivedi(auth.)
Severely acclaimed textual content for machine functionality analysis--now in its moment edition
the second one variation of this now-classic textual content offers a present and thorough remedy of queueing platforms, 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 sensible suggestions had to behavior functionality and reliability reviews of machine, conversation, and production systems.
beginning with uncomplicated chance thought, the textual content units the root for the extra advanced issues of queueing networks and Markov chains, utilizing functions and examples to demonstrate key issues. Designed to interact the reader and construct useful functionality research abilities, the textual content encompasses a wealth of difficulties that reflect real challenges.
New good points of the second one variation include:
* bankruptcy studying simulation tools and applications
* functionality research functions for instant, net, 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 recent and renowned 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 turning out to be complexity of laptop and communique structures, the necessity for this article, which expertly mixes concept and perform, is great. Graduate and complicated undergraduate scholars in machine technology will locate the broad use of examples and difficulties to be very important in gaining knowledge of either the fundamentals and the positive issues of the sphere, whereas execs will locate the textual content crucial for constructing structures that conform to criteria and laws.
also, an answer handbook and an FTP website with hyperlinks to author-provided info for the ebook 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 platforms (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):
Read or Download Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications, Second Edition PDF
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Additional info for Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications, Second Edition
Example text
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. 64) where s is a complex parameter. L x ( s ) is also called the Laplace-Stieltjes transform (LST) of the distribution function (CDF) and can also be written as 00 L x ( s )= /e-'"dFx(z).
P ( X 3 = 4) = i. 2 Conditional Probability # P ( X 1 = 2). P ( X 3 = 4) A conditional probability: P ( X 1 = 2 1 I X z = 5 2 , . . , x, = 2 , ) is the probability for X 1 = 2 1 under the conditions Then we get X2 = 2 2 , P ( X 1 = 2 1 I x2 = 2 2 , . . , x, = 2 , ) P ( X 1 = Z l r X 2 = 2 2 , . . ,X , = z , ) P ( X 2 = 2 2 , . . ,X , = 2 , ) X3 = 53, etc. 48) For continuous random variables we have P ( X 1 I 2 1 I xz I 2 2 , . 49) 34 INTRODUCTION We demonstrate this also with the preceding example of tossing a pair of dice.
If we merge n Poisson processes with distributions for the interarrival times 1 - e-’it, 1 5 i 5 n, into one single process, then the result is a Poisson process for which the interarrival times have the distribution 1 - e-’t with X = X i (see Fig. 5). cy=l fig. 5 Merging of Poisson processes. 2. 6. The exponential distribution has many useful properties with analytic tractability, but is not always a good approximation to the observed distribution. Experiments have shown deviations. For example, the coefficient of variation of the service time of a processor is often greater than one, and for a peripheral device it is usually less than one.