Beyond NP: Arc-Consistency for Quantified Constraints
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Constraint and Integer Programming in OPL
INFORMS Journal on Computing
GlueQoS: Middleware to Sweeten Quality-of-Service Policy Interactions
Proceedings of the 26th International Conference on Software Engineering
Web Services-Based Architecture for Reducing Behavior and Quality Uncertainties
E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
A Fuzzy Model for Selection of QoS-Aware Web Services
ICEBE '06 Proceedings of the IEEE International Conference on e-Business Engineering
A probabilistic approach to modeling and estimating the QoS of web-services-based workflows
Information Sciences: an International Journal
Solving quantified constraint satisfaction problems
Artificial Intelligence
A Probabilistic Framework for Decentralized Management of Trust and Quality
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
AMPol-Q: adaptive middleware policy to support qos
ICSOC'06 Proceedings of the 4th international conference on Service-Oriented Computing
A control-based middleware framework for quality-of-service adaptations
IEEE Journal on Selected Areas in Communications
Adaptation of web services based on QoS satisfaction
ICSOC'10 Proceedings of the 2010 international conference on Service-oriented computing
A fuzzy service adaptation based on QoS satisfaction
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
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In the context of service discovery, matchmakers check the compliance of service-level objectives from providers and consumers. The problem of bounded uncertainty arises if some property is non-fixable. In this case, the provider is not able to control the value it takes at runtime, so the eventual consumer must not have the choice to select a value and fix it, but only knowing the guaranteed range of values it may take. To the best of our knowledge, there does not exist any approach which deals with this scenario. Most matchmakers work as if all properties were fixable, and a few have assumed the contrary. In either case, the accuracy of their results is likely to be in question since there may be involved both fixable and non-fixable properties at the same time, and there may also exist dependencies between them. In order to improve the accuracy, we present a holistic approach to matchmaking under bounded uncertainty and propose constraint programming as our choice to deal with it, so that matchmaking is transformed into a quantified constraint satisfaction problem.