piwik-script

Deutsch Intern
    Chair of Computer Science II - Software Engineering

    DML

    Descartes Modeling Language

    The Descartes Modeling Language (DML) is an architecture-level modeling language for quality-of-service and resource management of modern dynamic IT systems and infrastructures. DML is designed to serve as a basis for self-aware systems management during operation, ensuring that system quality-of-service requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible. The term quality-of-service (QoS) is used to refer to performance (response time, throughput, scalability and efficiency) and dependability (availability, reliability and security). The current version of DML is focused on performance and availability, however, the modeling language itself is designed in a generic fashion and it is intended to eventually support further QoS properties.

    More information can be found on the following pages:

    If you have any questions, please contact Simon Eismann  or Johannes Grohmann.

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    Publications

    2019

    • Automation in Software Performance Engineering Based on a Declarative Specification of Concerns. J. C. Walter; (2019).
       
    • Online model learning for self-aware computing infrastructures. S. Spinner; J. Grohmann; S. Eismann; S. Kounev; in Journal of Systems and Software (2019). 147 1–16.
       
    • Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques. J. Grohmann; S. Eismann; S. Elflein; M. Mazkatli; J. von Kistowski; S. Kounev; in 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2019). 309–322.
       
    • Integrating Statistical Response Time Models in Architectural Performance Models. S. Eismann; J. Grohmann; J. Walter; J. von Kistowski; S. Kounev; in Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA) (2019). 71–80.
       

    2018

    • The Vision of Self-Aware Performance Models. J. Grohmann; S. Eismann; S. Kounev; in 2018 IEEE International Conference on Software Architecture Companion (ICSA-C) (2018). 60–63.
       
    • The Vision of Self-aware Reordering of Security Network Function Chains. L. Iffländer; J. Walter; S. Eismann; S. Kounev; in Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering (2018). 1–4.
       
    • Tools for Declarative Performance Engineering. J. Walter; S. Eismann; J. Grohmann; D. Okanovic; S. Kounev; in Companion of the 2018 ACM/SPEC International Conference on Performance Engineering (2018). 53–56.
       
    • Modeling of Parametric Dependencies for Performance Prediction of Component-based Software Systems at Run-time. S. Eismann; J. Walter; J. von Kistowski; S. Kounev; in 2018 IEEE International Conference on Software Architecture (ICSA) (2018). 135–144.
       
    • Black-box Learning of Parametric Dependencies for Performance Models. V. Ackermann; J. Grohmann; S. Eismann; S. Kounev; in Proceedings of 13th International Workshop on Models@run.time (MRT), co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018) (2018).
       
    • Flexible Performance Predictions at Run-time. S. Eismann; (2018, April).
       
    • TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research. J. von Kistowski; S. Eismann; N. Schmitt; A. Bauer; J. Grohmann; S. Kounev; in Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (2018). 223–236.
       

    2017

    • Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language. N. Huber; F. Brosig; S. Spinner; S. Kounev; M. Bähr; in IEEE Transactions on Software Engineering (TSE) (2017). 43(5) 432–452.
       
    • Design and Evaluation of a Proactive, Application-Aware Auto-Scaler. A. Bauer; N. Herbst; S. Kounev; in Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
       
    • Solving Explicit Dependencies for Architectural Performance Models. S. Eismann; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, März).
       
    • Mapping of Service Level Objectives to Performance Queries. J. Walter; D. Okanovic; S. Kounev; in Proceedings of the 2017 Workshop on Challenges in Performance Methods for Software Development (WOSP-C’17) co-located with 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
       
    • An Expandable Extraction Framework for Architectural Performance Models. J. Walter; C. Stier; H. Koziolek; S. Kounev; in Proceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS’17) (2017).
       
    • Automated and Adaptable Decision Support for Software Performance Engineering. J. Walter; A. van Hoorn; S. Kounev; in Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools (2017).
       
    • Providing Model-Extraction-as-a-Service for Architectural Performance Models. J. Walter; S. Eismann; N. Reed; S. Kounev; in Proceedings of the 2017 Symposium on Software Performance (SSP) (2017).
       
    • Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language. S. Kounev; N. Huber; F. Brosig; S. Spinner; M. Baehr; in Jan Jürjens, Kurt Schneider (Hrsg.): Software Engineering 2017 (SE 2017), Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-24. Februar 2017, Hannover, Germany (2017).
       

    2016

    • Transformation of Descartes Modeling Language to Queueing Networks. K. Dietz; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, September).
       
    • A Reference Architecture for Online Performance Model Extraction in Virtualized Environments. S. Spinner; J. Walter; S. Kounev; in Proceedings of the 2016 Workshop on Challenges in Performance Methods for Software Development (WOSP-C’16) co-located with 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016) (2016).
       
    • Chameleon: Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. A. Bauer; (2016, Oktober).
       
    • The Descartes Modeling Language for Self-Aware Performance and Resource Management. S. Kounev; (Z. M. (Jack) Jiang, Hrsg.) (2016, April).
       
    • A Model-Based Approach to Designing Self-Aware IT Systems and Infrastructures. S. Kounev; N. Huber; F. Brosig; X. Zhu; in IEEE Computer (2016). 49(7) 53–61.
       

    2015

    • Model-based Autonomic and Performance-aware System Adaptation in Heterogeneous Resource Environments: A Case Study. N. Huber; J. Walter; M. Bähr; S. Kounev; in Proceedings of the 2015 IEEE International Conference on Cloud and Autonomic Computing (ICCAC) (2015).
       
    • Automated Transformation of Descartes Modeling Language to Palladio Component Models. A. Hildebrandt; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2015, Januar).
       
    • Automated Transformation of Descartes Modeling Language to Palladio Component Model. J. Walter; S. Eismann; A. Hildebrandt; in Proceedings of the 2015 Symposium on Software Performance (SSP) (2015).
       
    • The Descartes Modeling Language for Self-Aware Performance and Resource Management. S. Kounev; (2015, März).
       
    • The Descartes Modeling Language for Self-Aware Performance and Resource Management. S. Kounev; F. Brosig; N. Huber; in Software Engineering 2015, Fachtagung des GI-Fachbereichs Softwaretechnik, 17. - 20. März 2015, Dresden, Deutschland (2015).
       

    2014

    • Descartes Network Infrastructures (DNI) Manual: Meta-models, Transformations, Examples P. Rygielski; S. Kounev; (2014).
       
    • Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures. N. Huber; (2014, Juli).
       
    • The Descartes Modeling Language S. Kounev; F. Brosig; N. Huber; (2014).
       
    • Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments. N. Huber; A. van Hoorn; A. Koziolek; F. Brosig; S. Kounev; in Service Oriented Computing and Applications Journal (SOCA) (2014). 8(1) 73–89.
       
    • Architecture-Level Software Performance Abstractions for Online Performance Prediction. F. Brosig; N. Huber; S. Kounev; in Elsevier Science of Computer Programming Journal (SciCo) (2014). Vol. 90, Part B 71–92.
       
    • Architecture-Level Software Performance Models for Online Performance Prediction. F. Brosig; (2014, Juli).
       

    2012

    • S/T/A: Meta-Modeling Run-Time Adaptation in Component-Based System Architectures. N. Huber; A. van Hoorn; A. Koziolek; F. Brosig; S. Kounev; in Proceedings of the 9th IEEE International Conference on e-Business Engineering (ICEBE 2012) (2012). 70–77.
       
    • Modeling Dynamic Virtualized Resource Landscapes. N. Huber; F. Brosig; S. Kounev; in Proceedings of the 8th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2012) (2012). 81–90.
       
    • Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models. F. Brosig; N. Huber; S. Kounev; in Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26--28, 2012, Bertinoro, Italy (2012).