Runtime Models as Interfaces for Adapting Software Systems Seminar on Software Engineering for Self-Adaptive Systems Schloss Dagstuhl, October 24-29, 2010 Thomas Vogel System Analysis and Modeling Group Hasso Plattner Institute University of Potsdam
Introduction The term Model-Driven Engineering (MDE) is typically used to describe software development approaches in which abstract models of software systems are created and systematically transformed to concrete implementations. . . . In our broad vision of MDE, models are not only the primary artifacts of development, they are also the primary means by which developers and other systems understand, interact with, configure and modify the runtime behavior of software . [France and Rumpe, 2007] • Runtime models as interfaces for adaptation • Typical or new MDE concepts and techniques for self-adaptive software systems (online vs. offline) Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 2
One Runtime Model as an Interface Autonomic Manager Autonomic Manager Analyze Plan Analyze Plan Knowledge Monitor Execute Knowledge ⇒ Monitor Execute Runtime Model Sensors Effectors Managed Element Sensors Effectors [Kephart and Chess, 2003] Managed Element Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 3
A Metamodel for a Runtime Model Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 4
A Metamodel for a Runtime Model simplified Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 4
Abstract Runtime Models complex detailed multiple concerns platform-specific solution space Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 5
Abstract Runtime Models vs. complex less complex detailed abstract multiple concerns one concern platform-specific platform-independent solution space problem space Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 5
Abstract Runtime Models vs. complex less complex detailed abstract multiple concerns one concern platform-specific platform-independent solution space problem space Metamodel for a Metamodel for a Source Model Target Model Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 5
MDE for Self-Adaptive Software Systems Different runtime models for monitoring architectural element Autonomic Manager model Analyze Plan monitoring • performance, adaptation Knowledge Monitor Execute defined by • failures, and uses • architectural constraints, Metamodel Target Model and for parameter and TGG Rules Model Transformation Engine Factories structural adaptation . Metamodel Source Model Incremental, bidirectional model synchronization based Sensors Effectors on Triple Graph Grammars Managed System (TGG). Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 6
Runtime Model Synchronization Monitoring Monitoring EJB System Adaptation Adaptation Further reading: ICAC’09, Models@run.time’09, SEAMS’10 Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 7
Ongoing and Future Work Which kind of models at which level of abstraction for which adaptation/management task? � Architecting self-adaptive systems Runtime Meta-Metamodels (M3) Runtime Metamodels (M2) Rules, Strategies, Constraints, Requirements and Goals Runtime Models (M1) Configuration Space and Variability Models Context Configuration and Architectural Models and Resource Models Implementation Models [Vogel et al., 2010b] [Kramer and Magee, 2007] Using models as interfaces for managed systems and within autonomic managers, how to specify the semantics of models and model operations? Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 8
Ongoing and Future Work cont’d Distributed Self-Adaptive Systems � Self-organizing systems • Distributed managed and managing systems • Distribution vs. consistency • Distributed models and MDE techniques • Specialized models for • monitoring and adapting managed systems • coordination between autonomic managers • . . . Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 9
Ongoing and Future Work cont’d Distributed Self-Adaptive Systems � Self-organizing systems • Distributed managed and managing systems ? • Distribution vs. consistency • Distributed models and MDE techniques • Specialized models for • monitoring and adapting managed systems • coordination between autonomic managers • . . . Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 9
References [France and Rumpe, 2007] France, R. and Rumpe, B. (2007). Model-driven Development of Complex Software: A Research Roadmap. In Proc. of the ICSE Workshop on Future of Software Engineering (FOSE) , pages 37–54. IEEE. [Kephart and Chess, 2003] Kephart, J. and Chess, D. (2003). The Vision of Autonomic Computing. IEEE Computer , 36(1):41–50. [Kramer and Magee, 2007] Kramer, J. and Magee, J. (2007). Self-Managed Systems: an Architectural Challenge. In Proc. of the ICSE Workshop on Future of Software Engineering , pages 259–268. IEEE. [Vogel and Giese, 2010] Vogel, T. and Giese, H. (2010). Adaptation and Abstract Runtime Models. In Proc. of the 5th ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) , pages 39–48. ACM. [Vogel et al., 2009] Vogel, T., Neumann, S., Hildebrandt, S., Giese, H., and Becker, B. (2009). Model-Driven Architectural Monitoring and Adaptation for Autonomic Systems. In Proc. of the 6th Intl. Conference on Autonomic Computing and Communications , pages 67–68. ACM. [Vogel et al., 2010a] Vogel, T., Neumann, S., Hildebrandt, S., Giese, H., and Becker, B. (2010a). Incremental Model Synchronization for Efficient Run-Time Monitoring . In Models in Software Engineering, Workshops and Symposia at MODELS 2009, Reports and Revised Selected Papers , volume 6002 of LNCS , pages 124–139. Springer. [Vogel et al., 2010b] Vogel, T., Seibel, A., and Giese, H. (2010b). Toward Megamodels at Runtime. In Proceedings of the 5th International MODELS Workshop on Models@run.time , volume 641 of CEUR Workshop Proceedings , pages 13–24. CEUR-WS.org. Thomas Vogel | Dagstuhl 10431 | October 24-29, 2010 10
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