Optimal Task Migration in Service-Oriented Systems: Algorithms and Mechanisms
Stein, Sebastian, Gerding, Enrico and Jennings, Nick (2010) Optimal Task Migration in Service-Oriented Systems: Algorithms and Mechanisms. At 19th European Conference on Artificial Intelligence (ECAI), Lisbon, Portugal, , 73-78.
- Accepted Manuscript
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.
|Item Type:||Conference or Workshop Item (Speech)|
|Divisions :||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
|Accepted Date and Publication Date:||
|Date Deposited:||05 May 2010 13:58|
|Last Modified:||31 Mar 2016 14:17|
EUROCORES LogiCCC (collaboration led by Dr. Edith Elkind): Computational Foundations of Social Choice.
Funded by: ESRC National Centre for Research Methods (RES-000-22-2731)
Led by: Edith Elkind
Led by: Enrico Harm Gerding
1 September 2008 to 29 February 2012
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