Queueing Networks and Markov Chains : Modeling and Performance Evaluation With Computer Science Applications [Hardcover]

Gunter Bolch (Author), Stefan Greiner (Author), Hermann de Meer (Author), Kishor Shridharbhai Trivedi (Author), Kishor S. Trivedi (Author)

Book Description

Publication Date: August 31, 1998 | ISBN-10: 0471193666 | ISBN-13: 978-0471193661

The first book to address computer performance evaluation from the perspective of queueing theory and Markov chains.

Queueing Networks and Markov Chains provides comprehensive coverage of the theory and application of computer performance evaluation based on queueing networks and Markov chains. Progressing from basic concepts to more complex topics, this book offers a clear and concise treatment of the state of the art in this important field.

Essential reading for system designers and researchers as well as graduate students taking courses in computer performance analysis, this book contains:
* A basic introduction to probability theory
* An explanation of the characteristics of different types of Markov chains
* Simple examples of all algorithms
* Transient and steady-state solution algorithms
* Well-known solution techniques for queueing systems and networks
* A broad range of application studies-from client-server systems to ATM networks
* Hundreds of illustrations, exercises, and more.

As computer and communications systems become more complex, system designers are increasingly called upon to locate information bottlenecks or create optimal systems for specific needs. In a short period of time, performance modeling techniques have become an important tool for this type of work-and indispensable to anyone dealing with questions of reliability and quality in operations, communications, and manufacturing. Queueing Networks and Markov Chains is an up-to-date, application-driven guide to computer performance analysis. It is the only book currently available that combines theory and applications of computer performance evaluation with queueing networks and Markov chains, and offers an abundance of performance-evaluation algorithms, applications, and case studies.

Entirely self-contained, Queueing Networks and Markov Chains introduces probability theory and clearly explains basic concepts before moving to advanced topics. It examines Markov chains and solution algorithms, building on results obtained in the Markov chain chapter to derive the basic relationship for queueing networks. Modeling and evaluation are discussed in the context of a variety of systems-including client-server systems, pulling systems, operating systems, ATM networks, and more. The authors present new queueing and optimization techniques for queueing networks, as well as multilevel methods for the solution of Markovian systems of equations. They show how to find an appropriate solution algorithm for a given problem using the queueing network tool PEPSY and how to determine benefits or limitations of queueing networks and Markov chains using the Markov analyzer MOSES.

In addition, the book provides numerous illustrations and exercises, gives simple examples for all algorithms, and compares various methods for their computation time, storage requirement, accuracy, and applicability.

Timely and comprehensive, Queueing Networks and Markov Chains is essential for practitioners and researchers working in this rapidly evolving field, as well as for graduate students in computer science departments.

Editorial Reviews

Review

"I can recommend this book as text for a course in queuing theory or performance analysis...also an excellent research book to have on the shelf or in the library." (Technometrics, February 2007)

"A valuable addition to the libraries of experienced practitioners, and an excellent course resource for students." (CHOICE, November 2006)

“Thoroughly updated with new content, as well as new problems and worked examples…” (Zentralblatt MATH, April 2007)

  --This text refers to an alternate Hardcover edition.

From the Publisher

Performance analysis seeks to discover the information bottlenecks in a computer system, and allows the system designer to create an optimal system for a specific need. This book, in the words of the authors, presents a "self-contained and complete presentation of the theory and application of computer performance evaluation based on queueing theory and Markov chains." After beginning with basic probability theory, Queueing Networks and Markov Chains proceeds to the more complicated topics of queueing networks and Markov chains, using applications and examples to illustrate key points.

See all Editorial Reviews

Product Details

• Hardcover: 726 pages
• Publisher: Wiley-Interscience (August 31, 1998)
• Language: English
• ISBN-10: 0471193666
• ISBN-13: 978-0471193661
• Product Dimensions: 9.6 x 6.6 x 1.8 inches
• Shipping Weight: 3 pounds
• Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #1,418,638 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

12 of 12 people found the following review helpful:

4.0 out of 5 stars Fine addition to the literature, April 28, 2001

By 

Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews
(VINE VOICE)    (HALL OF FAME REVIEWER)    (REAL NAME)   

This review is from: Queueing Networks and Markov Chains : Modeling and Performance Evaluation With Computer Science Applications (Hardcover)

The authors give a nice overview of computer performance evaluation using queueing theory and continuous and discrete-time Markov chains. After a short review of the relevant probability and statistics, the authors discuss Markov chains in the second chapter, pointing out that Markov processes can be used to model queueing systems even when these systems have behavior governed by non-exponential distributions. They characterize these as Markovizing methods. Their treatment of both discrete and continuous time Markov models is short but adequate, covering all the necessary concepts such as ergodicity and irreducibility. They then give a thorough discussion of the modeling process as actually done in practice. Their discussion of model sizing sets up their methodologies for dealing with large models later in the book. Performance measures for system requirements are discussed in terms of Markov reward models. Their treatment here is very detailed and they also give a large collection of helpful references on the subject.Petri nets are also discussed in the context of model generation. The authors state, correctly I think, that more time should be spent of developing models rather than the underlying mathematics. In their treatment of networks with non-exponential service time and interarrival time distributions, the authors employ the diffusion approximation via the solution of the Fokker-Planck equation. The don't discuss this in detail but give references for those who can read German. This would have been a place for a detailed analysis and derivation, given the surprising introduction of the Fokker-Planck equation in queueing theory. They also use, interestingly, maximum entropy methods to get approximate solutions of open and closed queueing networks. A very short chapter on optimization is given in the next chapter, which could stand to be more lengthy given the importance of this in implementing networks commercially. The next chapter covers some of the performance tools that are available for studying networks. The Performance Evaluation and Prediction System (PEPSY), stochastic Petri net package (SPNP), the CSPL language, the Model Description Language (MOSEL), the symbolic hierarchical automated reliability performance evaluator (SHARPE) are discussed with examples of each. Readers not having these tools will of course will not benefit too much from reading this chapter, except for maybe to get an idea of what is available. The OPNET and Ns-simulator packages,which are very nice modeling tools are not treated at all for some reason.

The last chapter covers applications, with case studies of queueing networks, Markov chains, stochastic Petri nets, and hierarchical models. Although of somewhat limited value in practice, the examples given do give the reader an idea of how the material in the book can be applied. And here again, the authors stress the use of modeling packages such as SHARPE and PEPSY, to verify the calculations in the case studies. They consider a closed non-product form queueing model of a medium-sized LAN in some detail with Ethernet links and a FDDI ring, solving it using Marie's method. Also interesting is their model of the UNIX operating system, which is also represented by a closed non-product queueing network. They compare the computation time needed to solve the model using CTMC, shadow, and DES techniques. Although the discussion is rather hurried, their model of an ATM network is also interesting, in that they use Markov reward models, obtaining both the state and transient solutions.

The book is one that will be of great assistance to those doing network modeling, performance analysis, and other time-scheduling modeling activiites. It is somewhat expensive, but worth the price I think considering the care which the authors take in their exposition.

8 of 9 people found the following review helpful:

5.0 out of 5 stars Review of Queueing Networks and Markov Chains, April 15, 2000

By A Customer

This review is from: Queueing Networks and Markov Chains : Modeling and Performance Evaluation With Computer Science Applications (Hardcover)

Queueing Networks and Markov Chains is a scholarly book written by internationally recognized experts in their field. The book provides both basic concepts and complex topics. The book is embellished with multiple illustrations, solved examples, and problems. In my opinion this book should be useful to researchers and practitioners of the queueing theory discipline as a convenient reference, and to students as a useful textbook. Look at the price! It's worth it.