By Gunter Bolch, Stefan Greiner, Hermann de Meer, Kishor S. Trivedi
Significantly acclaimed textual content for computing device functionality analysis--now in its moment edition
The moment version of this now-classic textual content presents a present and thorough remedy 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 bargains readers either the speculation and functional counsel had to behavior functionality and reliability reviews of computing device, conversation, and production systems.
Starting with easy chance thought, the textual content units the root for the extra complex issues of queueing networks and Markov chains, utilizing purposes and examples to demonstrate key issues. Designed to have interaction the reader and construct functional functionality research talents, the textual content incorporates a wealth of difficulties that reflect genuine challenges.
New beneficial properties of the second one version include:
* bankruptcy reading simulation equipment 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 answer options 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 transforming into complexity of machine and conversation structures, the necessity for this article, which expertly mixes conception and perform, is super. Graduate and complex undergraduate scholars in desktop technological know-how 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 sector, whereas pros will locate the textual content crucial for constructing structures that agree to criteria and regulations.
Additionally, an answer guide and an FTP website with hyperlinks to author-provided facts for the ebook can be found for deeper study.
About the author
GUNTER BOLCH, PhD, is educational Director within the division of machine technological know-how, collage of Erlangen. he's a coauthor of MOSEL, a strong specification language in accordance with Markov chains.He has released 5 textbooks and greater than a hundred thirty articles on functionality modeling of desktop and conversation structures and purposes.
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Additional resources for Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications (2nd Edition)
So is the time interval between the arrivals of two consecutive jobs at a computer system, or the throughput in such a system. The latter two examples can assume continuous values, whereas the first two only assume discrete values. Therefore, we have to distinguish between continuous and discrete random variables. 1 Discrete Random Variables A random variable that can only assume discrete values is called a discrete random variable, where the discrete values are often non-negative integers. The random variable is described by the possible values that it can assume and by the probabilities for each of these values.
4 ~ + are used [Aber94], when the sample size n is small. The i in Eq. 73) denotes the index of the failure in the ordered sample of failures. From the least-squares solution the Weibull parameters can then be computed by the back-transformation X = ebfaand cy = a. ” In the case of the Weibull distribution this is a coordinate system with logarithmic z-axis and double-logarithmic y-axis, in which the sample points are plotted. By eye-balling a straight line through the sample points one can determine the distribution parameters from the slope and the y-axis intercept.
Largeness Avoidance: Another way to deal with large models is to avoid the creation of such models from the beginning. The major largeness-avoidance technique we discuss in this book is that of product-form queueing networks. The main idea is, the structure of the underlying CTMC allows for an efficient solution that obviates the need for generation, storage, and solution of the large state space. The second method of avoiding largeness is to separate the originally single large problem into several smaller problems and to combine sub-model results into an overall solution.
Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications (2nd Edition) by Gunter Bolch, Stefan Greiner, Hermann de Meer, Kishor S. Trivedi