Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science ApplicationsCritically acclaimed text for computer performance analysis--now in its second edition The Second Edition of this now-classic text provides a current and thorough treatment of queueing systems, queueing networks, continuous and discrete-time Markov chains, and simulation. Thoroughly updated with new content, as well as new problems and worked examples, the text offers readers both the theory and practical guidance needed to conduct performance and reliability evaluations of computer, communication, and manufacturing systems. Starting with basic probability theory, the text sets the foundation for the more complicated topics of queueing networks and Markov chains, using applications and examples to illustrate key points. Designed to engage the reader and build practical performance analysis skills, the text features a wealth of problems that mirror actual industry challenges. New features of the Second Edition include: * Chapter examining simulation methods and applications * Performance analysis applications for wireless, Internet, J2EE, and Kanban systems * Latest material on non-Markovian and fluid stochastic Petri nets, as well as solution techniques for Markov regenerative processes * Updated discussions of new and popular performance analysis tools, including ns-2 and OPNET * New and current real-world examples, including DiffServ routers in the Internet and cellular mobile networks With the rapidly growing complexity of computer and communication systems, the need for this text, which expertly mixes theory and practice, is tremendous. Graduate and advanced undergraduate students in computer science will find the extensive use of examples and problems to be vital in mastering both the basics and the fine points of the field, while industry professionals will find the text essential for developing systems that comply with industry standards and regulations.
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Contents
1 | |
2 Markov Chains | 51 |
3 SteadyState Solutions of Markov Chains | 123 |
4 SteadyState AggregationDisaggregation Methods | 185 |
5 Transient Solution of Markov Chains | 209 |
6 Single Station Queueing Systems | 241 |
7 Queueing Networks | 321 |
8 Algorithms for ProductForm Networks | 369 |
9 Approximation Algorithms for ProductForm Networks | 421 |
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Common terms and phrases
algorithm analysis analyze applied approximation arrival process arrival rate assume batch blocking cache calculated closed network closed queueing network coefficient of variation computed condition consider convergence defined definition denotes derived determined DTMC estimate example exponentially distributed FCFS file finite firing first formula function given GSPN IEEE infinite input interarrival ith node job classes load-dependent lumpability macro-state marginal probabilities Markov chains mean number mean queue length mean response modified MOSEL-2 node normalization constant number of jobs obtain open network parameters Performance Evaluation performance measures Petri Nets phase phase-type distributions Poisson Poisson process priority probability vector Proc processor queueing network queueing network model queueing system random variable request reward rates routing probabilities Section server nodes service rates shown in Fig simulation solve specified SPNP station steady-state probability vector subnetwork Table task technique throughput tion transient underlying CTMC utilization visit ratios
Popular passages
Page 7 - Ability of a component or service to perform its required function at a stated instant or over a stated period of time.
Page 7 - Dependability is that property of a computing system which allows reliance to be justifiably placed on the service it delivers. The service delivered by a system is its behavior as it is perceived by its user(s); a user is another system (human or physical) which interacts with the former.