Dynamic Pricing and Automated Resource Allocation for Complex Information Services: Reinforcement Learning and Combinatorial Auctions
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.

1111359703
Dynamic Pricing and Automated Resource Allocation for Complex Information Services: Reinforcement Learning and Combinatorial Auctions
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.

129.99 In Stock
Dynamic Pricing and Automated Resource Allocation for Complex Information Services: Reinforcement Learning and Combinatorial Auctions

Dynamic Pricing and Automated Resource Allocation for Complex Information Services: Reinforcement Learning and Combinatorial Auctions

by Michael Schwind
Dynamic Pricing and Automated Resource Allocation for Complex Information Services: Reinforcement Learning and Combinatorial Auctions

Dynamic Pricing and Automated Resource Allocation for Complex Information Services: Reinforcement Learning and Combinatorial Auctions

by Michael Schwind

Paperback(2007)

$129.99 
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Overview

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.


Product Details

ISBN-13: 9783540680024
Publisher: Springer Berlin Heidelberg
Publication date: 03/28/2007
Series: Lecture Notes in Economics and Mathematical Systems , #589
Edition description: 2007
Pages: 295
Product dimensions: 6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

Dynamic Pricing and Automated Resource Allocation.- Empirical Assessment of Dynamic Pricing Preference.- Reinforcement Learning for Dynamic Pricing and Automated Resource Allocation.- Combinatorial Auctions for Resource Allocation.- Dynamic Pricing and Automated Resource Allocation Using Combinatorial Auctions.- Comparison of Reinforcement Learning and Combinatorial Auctions.
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