Algorithms and mechanisms for procuring services with uncertain durations using redundancy
OA Location
Author(s)
Stein, S
Gerding, E
Rogers, A
Larson, K
Jennings, N
Type
Journal 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).
Date Issued
2011-07-23
Date Acceptance
2011-07-15
Citation
Artificial Intelligence, 2011, 175 (14-15), pp.2021-2060
ISSN
1872-7921
Publisher
Elsevier
Start Page
2021
End Page
2060
Journal / Book Title
Artificial Intelligence
Volume
175
Issue
14-15
Copyright Statement
© 2011 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
http://eprints.soton.ac.uk/272655/
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mechanism design
Multi-agent systems
Service-oriented computing
Uncertainty
Redundancy
REAL-TIME TASKS
SYSTEMS
DESIGN
TRUST
OPTIMIZATION
RELIABILITY
ALLOCATION
EXECUTION
Artificial Intelligence & Image Processing
0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Publication Status
Published