Dynamic Pricing and Automated Resource Allocation for Complex Information Services (eBook)

Reinforcement Learning and Combinatorial Auctions

Michael Schwind (Autor)

eBook Download: PDF

2007 | 2007
XIV, 295 Seiten
Springer Berlin (Verlag)
978-3-540-68003-1 (ISBN)

Infos

This book develops allocation mechanisms that aim to ensure an efficient resource allocation in modern IT-services. Recent methods of artificial intelligence, such as neural networks and reinforcement learning, and nature-oriented optimization methods, such as genetic algorithms and simulated annealing, are advanced and applied to allocation processes in distributed IT-infrastructures, or grid systems.

Inhalt

Preface 5
Foreword Prof. Dr. König 7
Foreword Prof. Dr. Wendt 9
Contents 11
1 Introduction 15
1.1 Pricing of Distributed Information Services 15
1.2 Motivation 15
1.3 Structure of the Thesis 17
1.4 Methodology, Definitions and Scenario 19
1.5 Classic Economists and Paradigms in Pricing and Resource Allocation 25
2 Dynamic Pricing and Automated Resource Allocation 41
2.1 Dynamic Pricing 42
2.2 Automated Resource Allocation 48
2.3 Economic Resource Allocation in Distributed Computer Systems 54
2.4 Multi-Agent Systems and Automated Resource Allocation 72
2.5 Dynamic Pricing versus Automated Resource Allocation 77
3 Empirical Assessment of Dynamic Pricing Preference 81
3.1 Basic Data of the Study 81
3.2 Reference Groups Used in the Evaluation 85
3.3 Detailed Findings on Dynamic Pricing Preference 90
3.4 Empirical Analysis of Dynamic Pricing for ISIP Provision 96
3.5 Empirical Implications of Dynamic Pricing for ISIP Provision 101
4 Reinforcement Learning for Dynamic Pricing and Automated Resource Allocation 103
4.1 Basics of Reinforcement Learning 103
4.2 Basics of Yield Management 117
4.3 Dynamic Pricing, Scheduling, and Yield Management Using Reinforcement Learning 124
4.4 A Yield Maximizing Allocation System for a Single ISIP Resource 131
4.5 A Yield Maximizing Allocation System for Multiple ISIP Resources 138
5 Combinatorial Auctions for Resource Allocation 151
5.1 Basics of Combinatorial Auctions 152
5.2 Solving the Combinatorial Auction Problem 167
5.3 Design of Combinatorial Auctions 197
6 Dynamic Pricing and Automated Resource Allocation Using Combinatorial Auctions 205
6.1 Solving the Combinatorial Auction Problem in ISIP Environments 205
6.2 An Agent-Based Simulation Environment for Combinatorial Scheduling 217
6.3 Experimental Settings and Results 226
7 Comparison of Reinforcement Learning and Combinatorial Auctions 245
7.1 Properties of Reinforcement Learning and Combinatorial Auctions for ISIP Provision 245
7.2 Main Results and Recommendations 249
7.3 Outlook and Future Research 253
A Appendix 257
Internet Survey Questionnaire 257
List of Symbols 261
List of Figures 267
List of Tables 271
Glossary 273
References 277
Index 301

Produkt-Details

• Erscheint lt. Verlag: 24.4.2007
• Reihe/Serie: Lecture Notes in Economics and Mathematical Systems
• Reihe/Serie: Lecture Notes in Economics and Mathematical Systems
• Zusatzinfo: XIV, 295 p.
• Verlagsort: Berlin
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik
• Themenwelt: Technik
• Themenwelt: Wirtschaft ► Allgemeines / Lexika
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Wirtschaftsinformatik
• Themenwelt: Wirtschaft ► Volkswirtschaftslehre
• Schlagworte: algorithms • Artificial Intelligence • Automated Resource Allocation • Combinatorial Auctions • Dynamic Pricing • Genetic algorithms • Information Services • Intelligence • learning • Optimization • Optimization Methods • Reinforcement Learning
• ISBN-10: 3-540-68003-9 / 3540680039
• ISBN-13: 978-3-540-68003-1 / 9783540680031