Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Population-based incremental learning with memory scheme for changing environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
International Journal of High Performance Computing Applications
Using Blackboards to Optimize Grid Workflows with Respect to Quality Constraints
GCCW '06 Proceedings of the Fifth International Conference on Grid and Cooperative Computing Workshops
Grid workflow optimization regarding dynamically changing resources and conditions
Concurrency and Computation: Practice & Experience - 2nd International Workshop on Workflow Management and Applications in Grid Environments (WaGe2007)
SENECA – simulation of algorithms for the selection of web services for compositions
TES'05 Proceedings of the 6th international conference on Technologies for E-Services
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A Business Rules Driven Framework for Consumer-Provider Contracting of Web Services
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Quality-of-Service (QoS) aware service selectionproblems are a crucial issue in both Grids and distributed, service-oriented systems. When several implementations perservice exist, one has to be selected for each workflow step. Several heuristics have been proposed, including blackboardand genetic algorithms. Their applicability and performancehas already been assessed for static systems. In order to coverreal-world scenarios, the approaches are required to deal withdynamics of distributed systems. In this paper, we proposea representation of these dynamic aspects and enhance ouralgorithms to efficiently capture them. The algorithms areevaluated in terms of scalability and runtime performance, taking into account their adaptability to system changes. Bycombining both algorithms, we envision a global approach toQoS-aware service selection applicable to static and dynamicsystems. We prove our hypothesis by deploying the algorithmsin a Cloud environment (Google App Engine) that allows tosimulate and evaluate different system configurations.