Dynamic Pricing and Automated Resource Allocation for Complex Information Services

Reinforcement Learning and Combinatorial Auctions

  • Michael Schwind

Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 589)

About this book

Introduction

Many firms provide their customers with online information products which require limited resources such as server capacity. 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, e.g. grid systems. The author presents two methods, both of which using the users’ willingness-to-pay to control the allocation process: The first approach uses a yield management method that tries to learn an optimal acceptance strategy for resource requests. The second method is a combinatorial auction able to deal with resource complementarities. The author finally generates a method to calculate dynamic resource prices, marking an important step towards the industrialization of grid systems.

Keywords

Optimization Methods algorithms artificial intelligence genetic algorithms intelligence learning optimization

Authors and affiliations

  • Michael Schwind
    • 1
  1. 1.Faculty of Economics and Business AdministrationJohann Wolfgang Goethe UniversityFrankfurt am MainGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-68003-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-540-68002-4
  • Online ISBN 978-3-540-68003-1
  • Series Print ISSN 0075-8442
  • About this book