An Architectural Model-Based Approach toQuality-aware DevOps in Cloud Applications

Cloud-based software applications are designed to change often and rapidly during operations to provide constant quality of service. As a result the boundary between development and operations is becoming increasingly blurred. DevOps is a set of practices for the integrated consideration of developing and operating software. Software architecture is a central artifact in DevOps practices. Architectural information must be available during operations. Existing architectural models used in the development phase differ from those used in the operation phase in terms of abstraction, purpose and content. This chapter presents the iObserve approach to address these differences and allow for phase-spanning usage of architectural models.

[1]  Walter R. Bischofberger,et al.  Sotograph - A Pragmatic Approach to Source Code Architecture Conformance Checking , 2004, EWSA.

[2]  Stephan Seifermann Architectural Data Flow Analysis , 2016, 2016 13th Working IEEE/IFIP Conference on Software Architecture (WICSA).

[3]  Robert Heinrich,et al.  Architecture-based assessment and planning of change requests , 2015, 2015 11th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA).

[4]  Wil M. P. van der Aalst,et al.  Time prediction based on process mining , 2011, Inf. Syst..

[5]  Brice Morin,et al.  Models@ Run.time to Support Dynamic Adaptation , 2009, Computer.

[6]  Wilhelm Hasselbring,et al.  Kieker: a framework for application performance monitoring and dynamic software analysis , 2012, ICPE '12.

[7]  Liming Zhu,et al.  DevOps - A Software Architect's Perspective , 2015, SEI series in software engineering.

[8]  Robert Heinrich,et al.  Deriving Work Plans for Solving Performance and Scalability Problems , 2014, EPEW.

[9]  Christoph Wysseier,et al.  Visualizing live software systems in 3D , 2006, SoftVis '06.

[10]  Wilhelm Hasselbring Component-Based Software Engineering , 2002 .

[11]  Klaus Pohl,et al.  A Runtime Model Approach for Data Geo-location Checks of Cloud Services , 2014, ICSOC.

[12]  Michele Lanza,et al.  Visualizing Software Systems as Cities , 2007, 2007 4th IEEE International Workshop on Visualizing Software for Understanding and Analysis.

[13]  Oscar Nierstrasz,et al.  A Unified Approach to Architecture Conformance Checking , 2015, 2015 12th Working IEEE/IFIP Conference on Software Architecture.

[14]  Robert Heinrich,et al.  A Modular Reference Structure for Component-based Architecture Description Languages , 2015, ModComp@MoDELS.

[15]  Sam Newman,et al.  Building Microservices , 2015 .

[16]  Klaus Pohl,et al.  Runtime Model-Based Privacy Checks of Big Data Cloud Services , 2015, ICSOC.

[17]  Heiko Koziolek,et al.  CoCoME - The Common Component Modeling Example , 2007, CoCoME.

[18]  Uwe Zdun,et al.  Systematic literature review of the objectives, techniques, kinds, and architectures of models at runtime , 2016, Software & Systems Modeling.

[19]  Heiko Koziolek,et al.  PerOpteryx: automated application of tactics in multi-objective software architecture optimization , 2011, QoSA-ISARCS '11.

[20]  Peyman Oreizy,et al.  Runtime software adaptation: framework, approaches, and styles , 2008, ICSE Companion '08.

[21]  Gordon S. Blair,et al.  Models@ run.time , 2009, Computer.

[22]  Maria Luisa Villani,et al.  A framework for QoS-aware binding and re-binding of composite web services , 2008, J. Syst. Softw..

[23]  하수철,et al.  [서평]「Component Software」 - Beyond Object-Oriented Programming - , 2000 .

[24]  Sahin Albayrak,et al.  Extending Model Synchronization Results from Triple Graph Grammars to Multiple Models , 2016, ICMT.

[25]  Hong Yan,et al.  Discovering Architectures from Running Systems , 2006, IEEE Transactions on Software Engineering.

[26]  Robert Heinrich,et al.  Model-driven Instrumentation with Kieker and Palladio to Forecast Dynamic Applications , 2013, KPDAYS.

[27]  Dragan Ivanovic,et al.  Constraint-Based Runtime Prediction of SLA Violations in Service Orchestrations , 2011, ICSOC.

[28]  Wilhelm Hasselbring,et al.  Architectural run-time models for operator-in-the-loop adaptation of cloud applications , 2015, 2015 IEEE 9th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Environments (MESOCA).

[29]  Wilhelm Hasselbring,et al.  Cloud User-Centric Enhancements of the Simulator CloudSim to Improve Cloud Deployment Option Analysis , 2012, ESOCC.

[30]  Ricardo Terra,et al.  Static Architecture-Conformance Checking: An Illustrative Overview , 2010, IEEE Software.

[31]  Wilhelm Hasselbring,et al.  Integrating Run-time Observations and Design Component Models for Cloud System Analysis , 2014, Models@run.time.

[32]  Jean-Marie Favre,et al.  Foundations of Model ( Driven ) ( Reverse ) Engineering Episode I : Story of The Fidus Papyrus and the Solarus , 2004 .

[33]  Jan Jürjens,et al.  A Platform for Empirical Research on Information System Evolution , 2015, SEKE.

[34]  Robert Heinrich,et al.  The CoCoME Platform for Collaborative Empirical Research on Information System Evolution : Evolution Scenarios in the Second Founding Period of SPP 1593 , 2018 .

[35]  Mary Shaw,et al.  Engineering Self-Adaptive Systems through Feedback Loops , 2009, Software Engineering for Self-Adaptive Systems.

[36]  Klaus Pohl,et al.  ¨ INFORMATIK INSTITUT F UR iObserve : Integrated Observation and iObserve : Integrated Observation and Modeling Techniques to Support Adaptation and Evolution of Software , 2013 .

[37]  Robert Heinrich Architectural Run-time Models for Performance and Privacy Analysis in Dynamic Cloud Applications? , 2016, PERV.

[38]  Wilhelm Hasselbring,et al.  Software landscape and application visualization for system comprehension with ExplorViz , 2017, Inf. Softw. Technol..

[39]  Hui Song,et al.  Supporting runtime software architecture: A bidirectional-transformation-based approach , 2011, J. Syst. Softw..