The University of Southampton
University of Southampton Institutional Repository

Algorithms and mechanisms for procuring services with uncertain durations using redundancy

Algorithms and mechanisms for procuring services with uncertain durations using redundancy
Algorithms and mechanisms for procuring services with uncertain durations using redundancy
In emerging service-oriented systems, such as computational clouds or grids, software agents are able to automatically procure distributed services to complete computational tasks. However, service execution times are often highly uncertain and service providers may have incentives to lie strategically about this uncertainty to win more customers. In this paper, we argue that techniques from the field of artificial intelligence are instrumental to addressing these challenges.

To this end, we first propose a new decision-theoretic algorithm that allows a single service consumer agent to procure services for a computational task with a strict deadline. Crucially, this algorithm uses redundancy in a principled manner to mitigate uncertain execution times and maximise the consumer's expected utility. We present both an optimal variant that uses a novel branch-and-bound formulation, and a fast heuristic that achieves near-optimal performance. Using simulations, we demonstrate that our algorithms outperform approaches that do not employ redundancy by up to 130% in some settings.

Next, as the algorithms require private information about the providers? capabilities, we show how techniques from mechanism design can be used to incentivise truthfulness. As no existing work in this area deals with uncertain execution times and redundant invocations, we extend the state of the art by proposing a number of payment schemes for these settings. In a detailed analysis, we prove that our mechanisms fulfil a range of desirable economic properties, including incentive compatibility, and we discuss suboptimal variants that scale to realistic settings with hundreds of providers. We show experimentally that our mechanisms extract a high surplus and that even our suboptimal variants typically achieve a high efficiency (95% or more in a wide range of settings).
mechanism design, multi-agent systems, service-oriented computing, uncertainty, redundancy
2021-2060
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Larson, Kate
e180cd56-8fad-4e90-8e0c-00bd832ab254
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Larson, Kate
e180cd56-8fad-4e90-8e0c-00bd832ab254
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Stein, Sebastian, Gerding, Enrico, Rogers, Alex, Larson, Kate and Jennings, Nick (2011) Algorithms and mechanisms for procuring services with uncertain durations using redundancy. Artificial Intelligence, 175 (14-15), 2021-2060. (doi:10.1016/j.artint.2011.07.002).

Record type: Article

Abstract

In emerging service-oriented systems, such as computational clouds or grids, software agents are able to automatically procure distributed services to complete computational tasks. However, service execution times are often highly uncertain and service providers may have incentives to lie strategically about this uncertainty to win more customers. In this paper, we argue that techniques from the field of artificial intelligence are instrumental to addressing these challenges.

To this end, we first propose a new decision-theoretic algorithm that allows a single service consumer agent to procure services for a computational task with a strict deadline. Crucially, this algorithm uses redundancy in a principled manner to mitigate uncertain execution times and maximise the consumer's expected utility. We present both an optimal variant that uses a novel branch-and-bound formulation, and a fast heuristic that achieves near-optimal performance. Using simulations, we demonstrate that our algorithms outperform approaches that do not employ redundancy by up to 130% in some settings.

Next, as the algorithms require private information about the providers? capabilities, we show how techniques from mechanism design can be used to incentivise truthfulness. As no existing work in this area deals with uncertain execution times and redundant invocations, we extend the state of the art by proposing a number of payment schemes for these settings. In a detailed analysis, we prove that our mechanisms fulfil a range of desirable economic properties, including incentive compatibility, and we discuss suboptimal variants that scale to realistic settings with hundreds of providers. We show experimentally that our mechanisms extract a high surplus and that even our suboptimal variants typically achieve a high efficiency (95% or more in a wide range of settings).

Text
SteinAIJ.pdf - Accepted Manuscript
Download (791kB)

More information

e-pub ahead of print date: 23 July 2011
Published date: September 2011
Keywords: mechanism design, multi-agent systems, service-oriented computing, uncertainty, redundancy
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 272655
URI: http://eprints.soton.ac.uk/id/eprint/272655
PURE UUID: cfb15893-f508-4d89-9a73-4129cbef417c
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X

Catalogue record

Date deposited: 11 Aug 2011 12:53
Last modified: 15 Mar 2024 03:30

Export record

Altmetrics

Contributors

Author: Sebastian Stein ORCID iD
Author: Enrico Gerding ORCID iD
Author: Alex Rogers
Author: Kate Larson
Author: Nick 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.

×