KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association ARCHITECTURE-DRIVEN REQUIREMENTS ENGINEERING GROUP INSTITUTE FOR PROGRAM STRUCTURES AND DATA ORGANIZATION, FACULTY OF INFORMATICS are.ipd.kit.edu Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Overview Software Architecture Optimization at Design Time Modelling Degrees of Freedom Abstract from what is changed and optimize Define search space Evaluation function using model transformations When to make decisions At design time At human-involved evolution time At re-optimization time, e.g. daily At “run time”, e.g. within minutes or seconds

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Example: Business Reporting System Web-based system to generate business reports Relevant quality attributes Performance, reliability, security, costs Select quality requirements (QR), no levels yet Response time of reporting service shall be low The system shall reliably produce the expected result in most cases The system shall be secured against unauthorized access. The system shall have low development and operating costs. Possible initial prioritization

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Design Architecture and Evaluate Design and model architecture Annotate new components Reuse values for existing components Evaluate architecture with tools Predict performance, reliability avg(RT(reporting service)) = 3 sec, p(success) = 99.7%, Costs: 1000 K€

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Explore Design Space Degrees of Freedom (DoF) span design space Generic DoF of software architectures, e.g. deployment of components Operationalization of non-quantifiable QR, e.g. different authorization options Functional requirements (FR) to explore, e.g. a FR “deviation analysis”  Any decision? Model changes

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Explore Design Space Model changes Response time in sec Costs in T€ P(fail) DSE Degrees of Freedom (DoF) span design space Generic DoF of software architectures, e.g. deployment of components Operationalization of non-quantifiable QR, e.g. different authorization options Functional requirements (FR) to explore, e.g. a FR “deviation analysis”  Any decision?

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Determine optimal trade-offs Optimal candidates for performance and costs (rel. 99.8%) Optimal candidates in all three dimensions (rel. 99.9%)

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Existing Degrees of Freedom To model types of changes Metamodel for degrees of freedom For arbitrary CBA Metamodels Hardware Allocation of components Hardware configuration Number of servers Software Component selection Platform / MW selection Component replication Software configuration Custom Metamodell specific Project specific [ICPE2010,CBSE2011]

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Design Space spans [ICPE2010,CBSE2011] Hid processor configuration of S 4 and 6 allocations degrees of freedom Degree of freedom model Component allocation Processor configuration Component selection …

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Encoding in the design space Candidate 0 WS Tomcat Alloc. WS S1 Alloc. DB S3 … [ICPE2010,CBSE2011] Candidate 5 WS IIS Alloc. WS S2 Alloc. DB S3 …

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Decoding and Evaluation [Quasoss2009,ICPE2010] Candidate 5 (…) Mean resp. time 3,5s P(fail) 0,08% Costs € Model transformations based on degree of freedom modell Quality analyses Evaluation function Candidate 5 WS IIS Alloc. WS S2 Alloc. DB S3 …

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Optimization [Quasoss2009,ICPE2010] Candidate 5 (…) Antwortzeit 3,5s P(Ausfall) 0,08% Kosten € Model transformations based on degree of freedom modell Quality analyses Evaluation function Candidate 5 WS IIS Alloc. WS S2 Alloc. DB S3 …

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek SWA-based Quality Evaluation PCM2Cost [ICPE2010] Costs € PCM2DTMC [Brosch2009] P(fail) 0,1% PCM2 LQN [H. Koziolek 2008] Mean response time 3s Architecture model Model trans- formation in analysis model, prediction

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Predicting or Self-predictive? “Self-predictive: Able to predict the effect of dynamic changes (e.g., changing workloads) as well as predict the effect of possible adaptation actions (e.g., adding/removing resources)” (seminar page) Need to predict the effect at design time first, are goals attainable at all? Decide whether to fix design or to keep some degrees of freedom for later (at run time) Move decisions to runtime if Want to save operating costs? State space too large? Unanticipated change (can that be?) Run time costs to determine better (or optimal) solution acceptable

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Design time, Run time, …? Run time Design time Self adaptation loop Quick reaction to changing workload, predefined rules? Evolution loop (human redesign) Re-optimization loop (automated) Which degrees of freedom to consider? How “globally” to optimize? Meeting point of design time and run time techniques? -MDD techniques -Rules for inner loop, rare, costly Feedback (from monitoring or user) Similar to organic computing levels

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Conclusions & Outlook Software Architecture Optimization at Design Time Modelling Degrees of Freedom Abstract from what is changed and optimize Define search space Evaluation function using model transformations When to make decisions At design time At human-involved evolution time At re-optimization time, e.g. daily At “run time”, e.g. within minutes or seconds Apply to your self-aware systems approach? Looking forward to discuss with you

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek PerOpteryx References [WCOP2008] Anne Martens and Heiko Koziolek. Performance-oriented Design Space Exploration. In Proceedings of the Thirteenth International Workshop on Component-Oriented Programming (WCOP'08), Karlsruhe, Germany, pages 25-32, [FESCA2009] Anne Martens and Heiko Koziolek. Automatic, model-based software performance improvement for component- based software designs. In Proceedings of the Sixth International Workshop on Formal Engineering approches to Software Components and Architectures (FESCA 2009), volume 253 of Electronic Notes in Theoretical Computer Science, pages Elsevier, [Quasoss2009] Anne Martens, Franz Brosch, and Ralf Reussner. Optimising multiple quality criteria of service-oriented software architectures. In Proceedings of the 1st international workshop on Quality of service-oriented software systems (QUASOSS), pages ACM, New York, NY, USA, [Quasoss2010] Qais Noorshams, Anne Martens, and Ralf Reussner. Using quality of service bounds for effective multi-objective software architecture optimization. In QUASOSS '10: Proceedings of the 2nd International Workshop on the Quality of Service- Oriented Software Systems, pages 1:1-1:6. ACM, New York, NY, USA, [ICPE2010] ] Anne Martens, Heiko Koziolek, Steffen Becker, and Ralf H. Reussner. Automatically improve software models for performance, reliability and cost using genetic algorithms. In WOSP/SIPEW '10: Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering, pages , New York, NY, USA, ACM. [FASE2010] Vittorio Cortellessa, Anne Martens, Ralf Reussner, and Catia Trubiani. A process to effectively identify guilty performance antipatterns. In David Rosenblum and Gabriele Taentzer, editors, Fundamental Approaches to Software Engineering, 13th International Conference, FASE 2010, pages Springer-Verlag Berlin Heidelberg, [QoSA2010] Anne Martens, Danilo Ardagna, Heiko Koziolek, Raffaela Mirandola, and Ralf Reussner. A Hybrid Approach for Multi- Attribute QoS Optimisation in Component Based Software Systems. In George Heineman, Jan Kofron, and Frantisek Plasil, editors, Research into Practice - Reality and Gaps (Proceeding of QoSA 2010), volume 6093 of LNCS, pages Springer-Verlag Berlin Heidelberg, [ICPE2011] Catia Trubiani and Anne Koziolek. Detection and solution of software performance antipatterns in palladio architectural models. In Proceeding of the second joint WOSP/SIPEW international conference on Performance engineering, ICPE '11, pages ACM, New York, NY, USA, ICPE best paper award. [QoSA2011] Anne Koziolek, Heiko Koziolek, and Ralf Reussner. Peropteryx: Automated application of tactics in multi-objective software architecture optimization. In Proceedings of the Seventh International ACM Sigsoft Conference on the Quality of Software Architectures (QoSA 2011), Boulder, Colorado, USA, June ACM, New York, NY, USA. [CBSE2011] Anne Koziolek and Ralf Reussner. Towards a generic quality optimisation framework for component-based system models. In Proceedings of the 14th International Symposium on Component Based Software Engineering (CBSE 2011), Boulder, Colorado, USA, June ACM, New York, NY, USA.

Architecture-driven Requirements Engineering Group Institute for Program Structures and Data Organization Interplay of Design Time Optimization and Run Time Optimization Anne Koziolek Other References [Aleti2009] Aleti, A., Björnander, S., Grunske, L., and Meedeniya, I. (2009a). Archeopterix: An extendable tool for architecture optimization of AADL models. In Proc. of ICSE 2009 Workshop on Model-Based Methodologies for Pervasive and Embedded Software (MOMPES), pages IEEE Computer Society. [Becker2007] Becker, S., Koziolek, H., and Reussner, R.: Model-based Performance Prediction with the Palladio Component Model. In WOSP '07: Proceedings of the 6th International Workshop on Software and performance, pages 54-65, New York, NY, USA, February ACM. [Brosch 2009] Franz Brosch, Heiko Koziolek, Barbora Buhnova, and Ralf Reussner. Architecture-based reliability prediction with the palladio component model. Transactions on Software Engineering, 38(6), 2011, IEEE Computer Society. [Canfora2008] Canfora, G., Penta, M. D., Esposito, R., and Villani, M. L. (2008). A framework for qos-aware binding and re-binding of composite web services. Journal of Systems and Software, 81(10): [Diaz-Pace2008] Diaz Pace, A., Kim, H., Bass, L., Bianco, P., and Bachmann, F. (2008). Integrating quality- attribute reasoning frameworks in the archE design assistant. In Proceedings of the 4th International Conference on the Quality of Software-Architectures (QoSA 2008), volume 5281 of Lecture Notes in Computer Science, pages 171{188. Springer-Verlag, Berlin, Germany. [H. Koziolek2008] Heiko Koziolek and Ralf Reussner. A model-transformation from the palladio component model to layered queueing networks. In Proc. of the SPEC International Workshop on Performance Engineering (SIPEW'08), volume 5119 of LNCS, pages Springer, June [Menasce2010] Menasce, D. A., Casalicchio, E., and Dubey, V. (2010). On optimal service selection in service oriented architectures. Performance Evaluation, 67(8): Special Issue on Software and Performance. [Zeng2008] Zeng, L., Ngu, A., Benatallah, B., Podorozhny, R., and Lei, H. (2008). Dynamic composition and optimization of web services. Distributed and Parallel Databases, 24: