The University of Southampton
University of Southampton Institutional Repository

Scalable Mechanism Design for the Procurement of Services with Uncertain Durations

Scalable Mechanism Design for the Procurement of Services with Uncertain Durations
Scalable Mechanism Design for the Procurement of Services with Uncertain Durations
In this paper, we study a service procurement problem with uncertainty as to whether service providers are capable of completing a given task within a specified deadline. This type of setting is often encountered in large and dynamic multi-agent systems, such as computational Grids or clouds. To effectively deal with this uncertainty, the consumer may dynamically and redundantly procure multiple services over time, in order to increase the probability of success, while at the same time balancing this with the additional procurement costs. However, in order to do this optimally, the consumer requires information about the providers' costs and their success probabilities over time. This information is typically held privately by the providers and they may have incentives to misreport this, so as to increase their own profits. To address this problem, we introduce a novel mechanism that incentivises self-interested providers to reveal their true costs and capabilities, and we show that this mechanism is ex-post incentive compatible, efficient and individually rational. However, for these properties to hold, it generally needs to compute the optimal solution, which can be intractable in large settings. Therefore, we show how we can generate approximate solutions while maintaining the economic properties of the mechanism. This approximation admits a polynomial-time solution that can be computed in seconds even for hundreds of providers, and we demonstrate empirically that it performs as well as the optimal in typical scenarios. In particularly challenging settings, we show that it still achieves 97% or more of the optimal.
649-656
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Larson, Kate
e180cd56-8fad-4e90-8e0c-00bd832ab254
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Larson, Kate
e180cd56-8fad-4e90-8e0c-00bd832ab254
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Gerding, Enrico, Stein, Sebastian, Larson, Kate, Rogers, Alex and Jennings, Nicholas R. (2010) Scalable Mechanism Design for the Procurement of Services with Uncertain Durations. The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada. 10 - 14 May 2010. pp. 649-656 .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we study a service procurement problem with uncertainty as to whether service providers are capable of completing a given task within a specified deadline. This type of setting is often encountered in large and dynamic multi-agent systems, such as computational Grids or clouds. To effectively deal with this uncertainty, the consumer may dynamically and redundantly procure multiple services over time, in order to increase the probability of success, while at the same time balancing this with the additional procurement costs. However, in order to do this optimally, the consumer requires information about the providers' costs and their success probabilities over time. This information is typically held privately by the providers and they may have incentives to misreport this, so as to increase their own profits. To address this problem, we introduce a novel mechanism that incentivises self-interested providers to reveal their true costs and capabilities, and we show that this mechanism is ex-post incentive compatible, efficient and individually rational. However, for these properties to hold, it generally needs to compute the optimal solution, which can be intractable in large settings. Therefore, we show how we can generate approximate solutions while maintaining the economic properties of the mechanism. This approximation admits a polynomial-time solution that can be computed in seconds even for hundreds of providers, and we demonstrate empirically that it performs as well as the optimal in typical scenarios. In particularly challenging settings, we show that it still achieves 97% or more of the optimal.

Text
aamas2010.pdf - Accepted Manuscript
Download (176kB)

More information

Published date: May 2010
Additional Information: Event Dates: May 10-14, 2010
Venue - Dates: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, 2010-05-10 - 2010-05-14
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 268463
URI: http://eprints.soton.ac.uk/id/eprint/268463
PURE UUID: 7f75c2bd-5b9d-4a14-bec2-b93f8b58ec2a
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857

Catalogue record

Date deposited: 03 Feb 2010 15:46
Last modified: 15 Mar 2024 03:30

Export record

Contributors

Author: Enrico Gerding ORCID iD
Author: Sebastian Stein ORCID iD
Author: Kate Larson
Author: Alex Rogers
Author: Nicholas R. Jennings

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×