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SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
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Computer
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WOSP '07 Proceedings of the 6th international workshop on Software and performance
Performance Modeling and Evaluation of Distributed Component-Based Systems Using Queueing Petri Nets
IEEE Transactions on Software Engineering
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International Journal on Software Tools for Technology Transfer (STTT)
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SIPEW '08 Proceedings of the SPEC international workshop on Performance Evaluation: Metrics, Models and Benchmarks
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Journal of Systems and Software
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ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
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SCC '10 Proceedings of the 2010 IEEE International Conference on Services Computing
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QoSA'06 Proceedings of the Second international conference on Quality of Software Architectures
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Proceedings of the 15th ACM SIGSOFT symposium on Component Based Software Engineering
Locality-Awareness in a peer-to-peer publish/subscribe network
DAIS'12 Proceedings of the 12th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
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In this paper we propose an approach to integrate the use of time-related stochastic properties in a continuous design process based on models at runtime. Time-related specification of services are an important aspect of component-based architectures, for instance in distributed, volatile networks of computation nodes. The models at runtime approach eases the management of such architectures by maintaining abstract models of architectures synchronized with the physical, distributed execution platform. For self-adapting systems, prediction of delays and throughput of a component assembly is of utmost importance to take adaptation decision and accept evolutions that conform to time specifications. To this aim we define a metamodel extension based on stochastic Petri nets as an internal time model for prediction. We design a library of patterns to ease the specification and prediction of common time properties of models at runtime and make the synchronization of behaviors and structural changes easier. Our prediction engine is fast enough to perform prediction at runtime in a realistic setting and validate models at runtime.