Aufgabe der Performance-Analyse ist es, "Informationsstaus" in einem Computersystem aufzudecken, damit der Designer auf dieser Grundlage optimale Systeme für ganz spezifische Bedürfnisse erstellen kann. Hier erfahren Sie alles über Theorie und Anwendung von Performance-Bewertungen auf der Basis von Warteschlangen und Markov-Ketten. Zunächst behandeln die Autoren die Grundlagen der Wahrscheinlichkeitstheorie. Aufbauend darauf werden komplexere Themen anhand von Anwendungen und Beispielen anschaulich dargestellt. Speziell konzipiert für fortgeschrittene Studenten und Doktoranden. Ein Begleitbuch mit Übungsaufgaben und Lösungen ist in Planung. (11/98)…mehr
Aufgabe der Performance-Analyse ist es, "Informationsstaus" in einem Computersystem aufzudecken, damit der Designer auf dieser Grundlage optimale Systeme für ganz spezifische Bedürfnisse erstellen kann. Hier erfahren Sie alles über Theorie und Anwendung von Performance-Bewertungen auf der Basis von Warteschlangen und Markov-Ketten. Zunächst behandeln die Autoren die Grundlagen der Wahrscheinlichkeitstheorie. Aufbauend darauf werden komplexere Themen anhand von Anwendungen und Beispielen anschaulich dargestellt. Speziell konzipiert für fortgeschrittene Studenten und Doktoranden. Ein Begleitbuch mit Übungsaufgaben und Lösungen ist in Planung. (11/98)
GUNTER BOLCH, PhD, is Academic Director in the Department of Computer Science, University of Erlangen. He is a coauthor of MOSEL, a powerful specification language based on Markov chains.He has published five textbooks and more than 130 articles on performance modeling of computer and communication systems and applications. STEFAN GREINER, PhD, is Researcher at the DaimlerChrysler AG. He is coauthor of MOSEL and the recipient of the best paper award at the ESS94conference. HERMANN de MEER, PhD, is Professor in the Faculty of Mathematicsand Computer Science, University of Passau. He holds a chair in the Computer Networking and Communications Department; has published more than eighty peer-reviewed articles on performance modeling, computer networking, self-organization, quality of service, and peer-to-peer systems; and holds several international patents on related topics. KISHOR S. TRIVEDI, PhD, is Chaired Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science, Duke University. A Fellow of IEEE, he is the author of the bestselling textbook Probability and Statistics with Reliability, Queueing, and Computer Science Applications, Second Edition, published by Wiley.
Inhaltsangabe
Preface to the Second Edition. Preface to the First Edition. 1. Introduction. 1.1 Motivation. 1.2 Methodological Background. 1.3 Basics of Probability and Statistics. 2. Markov Chains. 2.1 Markov Processes. 2.2 Performance Measures. 2.3 Generation Methods. 3. Steady-State Solutions of Markov Chains. 3.1 Solution for a Birth Death Process. 3.2 Matrix-Geometric Method: Quasi-Birth-Death Process. 3.3 Hessenberg Matrix: Non-Markovian Queues. 3.4 Numerical Solution: Direct Methods. 3.5 Numerical Solution: Iterative Methods. 3.6 Comparison of Numerical Solution Methods. 4. Steady-State Aggregation/Disaggregation Methods. 4.1 Courtois' Approximate Method. 4.2 Takahashi's Iterative Method. 5. Transient Solution of Markov Chains. 5.1 Transient Analysis Using Exact Methods. 5.2 Aggregation of Stiff Markov Chains. 6. Single Station Queueing Systems. 6.1 Notation. 6.2 Markovian Queues. 6.3 Non-Markovian Queues. 6.4 Priority Queues. 6.5 Asymmetric Queues. 6.6 Queues with Batch Service and Batch Arrivals. 6.7 Retrial Queues. 6.8 Special Classes of Point Arrival Processes. 7. Queueing Networks. 7.1 Definitions and Notation. 7.2 Performance Measures. 7.3 Product-Form Queueing Networks. 8. Algorithms for Product-Form Networks. 8.1 The Convolution Algorithm. 8.2 The Mean Value Analysis. 8.3 Flow Equivalent Server Method. 8.4 Summary. 9. Approximation Algorithms for Product-Form Networks. 9.1 Approximations Based on the MVA. 9.2 Summation Method. 9.3 Bottapprox Method. 9.4 Bounds Analysis. 9.5 Summary. 10. Algorithms for Non-Product-Form Networks. 10.1 Nonexponential Distributions. 10.2 Different Service Times at FCFS Nodes. 10.3 Priority Networks. 10.4 Simultaneous Resource Possession. 10.5 Prograrns with Internal Concurrency. 10.6 Parallel Processing. 10.7 Networks with Asymmetric Nodes. 10.8 Networks with Blocking. 10.9 Networks with Batch Service. 11. Discrete-Event Simulation. 11.1 Introduction to Simulation. 11.2 Simulative or Analytic Solution? 11.3 Classification of Simulation Models. 11.4 Classification of Tools in DES. 11.5 The Role of Probability and Statistics in Simulation. 11.6 Applications. 12. Performance Analysis Tools. 12.1 PEPSY. 12.2 SPNP. 12. 3 MOSEL-2. 12.4 SHARPE. 12.5 Characteristics of Some Tools. 13. Applications. 13.1 Case Studies of Queueing Networks. 13.2 Case Studies of Markov Chains. 13.3 Case Studies of Hierarchical Models. Glossary. Bibliography. Index.
Preface to the Second Edition. Preface to the First Edition. 1. Introduction. 1.1 Motivation. 1.2 Methodological Background. 1.3 Basics of Probability and Statistics. 2. Markov Chains. 2.1 Markov Processes. 2.2 Performance Measures. 2.3 Generation Methods. 3. Steady-State Solutions of Markov Chains. 3.1 Solution for a Birth Death Process. 3.2 Matrix-Geometric Method: Quasi-Birth-Death Process. 3.3 Hessenberg Matrix: Non-Markovian Queues. 3.4 Numerical Solution: Direct Methods. 3.5 Numerical Solution: Iterative Methods. 3.6 Comparison of Numerical Solution Methods. 4. Steady-State Aggregation/Disaggregation Methods. 4.1 Courtois' Approximate Method. 4.2 Takahashi's Iterative Method. 5. Transient Solution of Markov Chains. 5.1 Transient Analysis Using Exact Methods. 5.2 Aggregation of Stiff Markov Chains. 6. Single Station Queueing Systems. 6.1 Notation. 6.2 Markovian Queues. 6.3 Non-Markovian Queues. 6.4 Priority Queues. 6.5 Asymmetric Queues. 6.6 Queues with Batch Service and Batch Arrivals. 6.7 Retrial Queues. 6.8 Special Classes of Point Arrival Processes. 7. Queueing Networks. 7.1 Definitions and Notation. 7.2 Performance Measures. 7.3 Product-Form Queueing Networks. 8. Algorithms for Product-Form Networks. 8.1 The Convolution Algorithm. 8.2 The Mean Value Analysis. 8.3 Flow Equivalent Server Method. 8.4 Summary. 9. Approximation Algorithms for Product-Form Networks. 9.1 Approximations Based on the MVA. 9.2 Summation Method. 9.3 Bottapprox Method. 9.4 Bounds Analysis. 9.5 Summary. 10. Algorithms for Non-Product-Form Networks. 10.1 Nonexponential Distributions. 10.2 Different Service Times at FCFS Nodes. 10.3 Priority Networks. 10.4 Simultaneous Resource Possession. 10.5 Prograrns with Internal Concurrency. 10.6 Parallel Processing. 10.7 Networks with Asymmetric Nodes. 10.8 Networks with Blocking. 10.9 Networks with Batch Service. 11. Discrete-Event Simulation. 11.1 Introduction to Simulation. 11.2 Simulative or Analytic Solution? 11.3 Classification of Simulation Models. 11.4 Classification of Tools in DES. 11.5 The Role of Probability and Statistics in Simulation. 11.6 Applications. 12. Performance Analysis Tools. 12.1 PEPSY. 12.2 SPNP. 12. 3 MOSEL-2. 12.4 SHARPE. 12.5 Characteristics of Some Tools. 13. Applications. 13.1 Case Studies of Queueing Networks. 13.2 Case Studies of Markov Chains. 13.3 Case Studies of Hierarchical Models. Glossary. Bibliography. Index.
Rezensionen
"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)
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