Service-Oriented Computing: Agents, Semantics, and - download pdf or read online

By António Luís Lopes, Luís Miguel Botelho (auth.), Jingshan Huang, Ryszard Kowalczyk, Zakaria Maamar, David Martin, Ingo Müller, Suzette Stoutenburg, Katia P. Sycara (eds.)

ISBN-10: 3540726187

ISBN-13: 9783540726180

ISBN-10: 3540726195

ISBN-13: 9783540726197

This publication constitutes the refereed lawsuits of the overseas Workshop on Service-Oriented Computing: brokers, Semantics, and Engineering, SOCASE 2007, held in Honolulu, hello, united states as an linked occasion of AAMAS 2007, the most overseas convention on self reliant brokers and multi-agent platforms. the quantity is rounded off with chosen 4 most sensible papers from the Service-Oriented Computing and Agent-based Engineering Workshop, SOCABE 2006, held at AAMAS 2006.

The 12 revised complete papers awarded have been conscientiously reviewed and chosen for inclusion within the publication. The papers hide a variety of themes on the intersection of service-oriented computing, semantic know-how and clever multiagent platforms, resembling carrier description and discovery; making plans, composition and negotiation; semantic techniques and repair brokers; in addition to applications.

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Example text

Fig. 3. 24% improvement over the old flexible strategy. 1, but now systematically introduce errors into the information that is available to a service consumer following the improved flexible strategy. To this end, we first evaluate the effect of relying on inaccurate failure probabilities, and then examine the impact of inaccurate service duration information. In both cases, we expect the performance of our strategy to decrease as the information becomes less accurate. However, because we rely on heuristic estimates, we anticipate that small inaccuracies will have little overall impact on the performance of the strategy.

To address this, we artificially increased our estimate for t˜ by a constant 20%, but such an approach still does not consider variance, and is not guaranteed to work well in all environments. An Effective Strategy for the Flexible Provisioning of Service Workflows 21 To overcome this, we now use an improved heuristic that does not assume a deterministic duration, but rather estimates the probability distribution of the workflow duration. To this end, we estimate the expected profit as: ∞ u˜ = p dW (x)u(x) dx − c˜, (4) 0 where dW (x) is a probability density function that estimates the overall duration of the workflow, and c˜ is an estimate of the expected overall cost (using a continuous probability function allows us to derive a simple and concise solution in closed form).

2. Net profit of flexible strategies In order to vary the performance characteristics of the tasks in the workflow, we randomly assign each to one of seven different types of tasks with varying costs (ci ) and duration distributions (Di ), as shown in Table 1. For every experimental run, we also attach a failure probability to each type (to determine fi ) that is drawn from a beta distribution with parameters α = 10·f and β = 10−α, where f is the average failure probability of the environment (unless f = 0 or f = 1, in which case all tasks have the same failure probability).

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Service-Oriented Computing: Agents, Semantics, and Engineering: AAMAS 2007 International Workshop, SOCASE 2007, Honolulu, HI, USA, May 14, 2007. Proceedings by António Luís Lopes, Luís Miguel Botelho (auth.), Jingshan Huang, Ryszard Kowalczyk, Zakaria Maamar, David Martin, Ingo Müller, Suzette Stoutenburg, Katia P. Sycara (eds.)


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