On-line Learning and the Metrical Task System Problem
Machine Learning
A framework for adaptive execution in grids
Software—Practice & Experience
Algorithms
Fault tolerant mechanism design
Artificial Intelligence
Journal of Artificial Intelligence Research
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Scalable mechanism design for the procurement of services with uncertain durations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Algorithms and mechanisms for procuring services with uncertain durations using redundancy
Artificial Intelligence
Reorganization of Agent Networks with Reinforcement Learning Based on Communication Delay
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Intelligent Decision Technologies
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In service-oriented systems, such as grids and clouds, users are able to outsource complex computational tasks by procuring resources on demand from remote service providers. As these providers typically display highly heterogeneous performance characteristics, service procurement can be challenging when the consumer is uncertain about the computational requirements of its task a priori. Given this, we here argue that the key to addessing this problem is task migration, where the consumer can move a partially completed task from one provider to another. We show that doing this optimally is NP-hard, but we also propose two novel algorithms, based on new and established search techniques, that can be used by an intelligent agent to efficiently find the optimal solution in realistic settings. However, these algorithms require full information about the providers' quality of service and costs over time. Critically, as providers are usually self-interested agents, they may lie strategically about these to inflate profits. To address this, we turn to mechanism design and propose a payment scheme that incentivises truthfulness. In empirical experiments, we show that (i) task migration results in an up to 160% improvement in utility, (ii) full information about the providers' costs is necessary to achieve this and (iii) our mechanism requires only a small investment to elicit this information.