Publisher's Synopsis
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.
