Publications

{Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language}. Kounev, Samuel; Huber, Nikolaus; Brosig, Fabian; Spinner, Simon; Baehr, Manuel; in Jan J{ü}rjens, Kurt Schneider (Hrsg.): Software Engineering 2017 (SE 2017), Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-24. Februar 2017, Hannover, Germany (2017). Gesellschaft f{ü}r Informatik (GI), Hannover, Germany.

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@inproceedings{KoHuBrSpBa2017-SE-DML,
  address = {Hannover, Germany},
  author = {Kounev, Samuel and Huber, Nikolaus and Brosig, Fabian and Spinner, Simon and Baehr, Manuel},
  booktitle = {Jan J{\"u}rjens, Kurt Schneider (Hrsg.): Software Engineering 2017 (SE 2017), Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-24. Februar 2017, Hannover, Germany},
  keywords = {descartes},
  month = {02},
  publisher = {Gesellschaft f{\"u}r Informatik (GI)},
  series = {Lecture Notes in Informatics (LNI)},
  title = {{Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language}},
  year = 2017
}

%0 Conference Paper
%1 KoHuBrSpBa2017-SE-DML
%A Kounev, Samuel
%A Huber, Nikolaus
%A Brosig, Fabian
%A Spinner, Simon
%A Baehr, Manuel
%B Jan J{ü}rjens, Kurt Schneider (Hrsg.): Software Engineering 2017 (SE 2017), Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-24. Februar 2017, Hannover, Germany
%C Hannover, Germany
%D 2017
%I Gesellschaft f{ü}r Informatik (GI)
%T {Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language}

{Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language}. Huber, Nikolaus; Brosig, Fabian; Spinner, Simon; Kounev, Samuel; B{ä}hr, Manuel; in IEEE Transactions on Software Engineering (TSE) (2017). 43(5) 432–452. IEEE Computer Society.

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Modern IT systems have increasingly distributed and dynamic architectures providing flexibility to adapt to changes in the environment and thus enabling higher resource efficiency. However, these benefits come at the cost of higher system complexity and dynamics. Thus, engineering systems that manage their end-to-end application performance and resource efficiency in an autonomic manner is a challenge. In this article, we present a holistic model-based approach for self-aware performance and resource management leveraging the Descartes Modeling Language (DML), an architecture-level modeling language for online performance and resource management. We propose a novel online performance prediction process that dynamically tailors the model solving depending on the requirements regarding accuracy and overhead. Using these prediction capabilities, we implement a generic model-based control loop for proactive system adaptation. We evaluate our model-based approach in the context of two representative case studies showing that with the proposed methods, significant resource efficiency gains can be achieved while maintaining performance requirements. These results represent the first end-to-end validation of our approach, demonstrating its potential for self-aware performance and resource management in the context of modern IT systems and infrastructures.

@article{HuBrSpKoBa2017-TSE-DML,
  abstract = {Modern IT systems have increasingly distributed and dynamic architectures providing flexibility to adapt to changes in the environment and thus enabling higher resource efficiency. However, these benefits come at the cost of higher system complexity and dynamics. Thus, engineering systems that manage their end-to-end application performance and resource efficiency in an autonomic manner is a challenge. In this article, we present a holistic model-based approach for self-aware performance and resource management leveraging the Descartes Modeling Language (DML), an architecture-level modeling language for online performance and resource management. We propose a novel online performance prediction process that dynamically tailors the model solving depending on the requirements regarding accuracy and overhead. Using these prediction capabilities, we implement a generic model-based control loop for proactive system adaptation. We evaluate our model-based approach in the context of two representative case studies showing that with the proposed methods, significant resource efficiency gains can be achieved while maintaining performance requirements. These results represent the first end-to-end validation of our approach, demonstrating its potential for self-aware performance and resource management in the context of modern IT systems and infrastructures.},
  author = {Huber, Nikolaus and Brosig, Fabian and Spinner, Simon and Kounev, Samuel and B{\"a}hr, Manuel},
  journal = {IEEE Transactions on Software Engineering (TSE)},
  keywords = {DQL},
  number = 5,
  pages = {432–452},
  publisher = {IEEE Computer Society},
  title = {{Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language}},
  volume = 43,
  year = 2017
}

%0 Journal Article
%1 HuBrSpKoBa2017-TSE-DML
%A Huber, Nikolaus
%A Brosig, Fabian
%A Spinner, Simon
%A Kounev, Samuel
%A B{ä}hr, Manuel
%D 2017
%I IEEE Computer Society
%J IEEE Transactions on Software Engineering (TSE)
%N 5
%P 432--452
%T {Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language}
%U http://dx.doi.org/10.1109/TSE.2016.2613863
%V 43
%X Modern IT systems have increasingly distributed and dynamic architectures providing flexibility to adapt to changes in the environment and thus enabling higher resource efficiency. However, these benefits come at the cost of higher system complexity and dynamics. Thus, engineering systems that manage their end-to-end application performance and resource efficiency in an autonomic manner is a challenge. In this article, we present a holistic model-based approach for self-aware performance and resource management leveraging the Descartes Modeling Language (DML), an architecture-level modeling language for online performance and resource management. We propose a novel online performance prediction process that dynamically tailors the model solving depending on the requirements regarding accuracy and overhead. Using these prediction capabilities, we implement a generic model-based control loop for proactive system adaptation. We evaluate our model-based approach in the context of two representative case studies showing that with the proposed methods, significant resource efficiency gains can be achieved while maintaining performance requirements. These results represent the first end-to-end validation of our approach, demonstrating its potential for self-aware performance and resource management in the context of modern IT systems and infrastructures.

{A Model-Based Approach to Designing Self-Aware IT Systems and Infrastructures}. Kounev, Samuel; Huber, Nikolaus; Brosig, Fabian; Zhu, Xiaoyun; in IEEE Computer (2016). 49(7) 53–61. IEEE.

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@article{KoHuBrZh2016-IEEEComputer-Descartes,
  author = {Kounev, Samuel and Huber, Nikolaus and Brosig, Fabian and Zhu, Xiaoyun},
  journal = {IEEE Computer},
  keywords = {Self-adaptive-systems},
  month = {07},
  number = 7,
  pages = {53-61},
  publisher = {IEEE},
  title = {{A Model-Based Approach to Designing Self-Aware IT Systems and Infrastructures}},
  volume = 49,
  year = 2016
}

%0 Journal Article
%1 KoHuBrZh2016-IEEEComputer-Descartes
%A Kounev, Samuel
%A Huber, Nikolaus
%A Brosig, Fabian
%A Zhu, Xiaoyun
%D 2016
%I IEEE
%J IEEE Computer
%N 7
%P 53-61
%T {A Model-Based Approach to Designing Self-Aware IT Systems and Infrastructures}
%U http://dx.doi.org/10.1109/MC.2016.198
%V 49

{Analysis of the Trade-offs in Different Modeling Approaches for Performance Prediction of Software Systems}. Kounev, Samuel; Brosig, Fabian; Meier, Philipp; Becker, Steffen; Koziolek, Anne; Koziolek, Heiko; Rygielski, Piotr; in Software Engineering 2016 (SE 2016), Fachtagung des GI-Fachbereichs Softwaretechnik, 23.-26. Feb 2016, Vienna, Austria (2016). 47–48. GI, Vienna, Austria.

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@inproceedings{KoBrMeBeKoKoRy2016-SE-tradeoffs_perf_prediction_approaches,
  address = {Vienna, Austria},
  author = {Kounev, Samuel and Brosig, Fabian and Meier, Philipp and Becker, Steffen and Koziolek, Anne and Koziolek, Heiko and Rygielski, Piotr},
  booktitle = {Software Engineering 2016 (SE 2016), Fachtagung des GI-Fachbereichs Softwaretechnik, 23.-26. Feb 2016, Vienna, Austria},
  keywords = {Formal_architecture_modeling},
  month = {02},
  pages = {47–48},
  publisher = {GI},
  series = {Lecture Notes in Informatics (LNI)},
  title = {{Analysis of the Trade-offs in Different Modeling Approaches for Performance Prediction of Software Systems}},
  year = 2016
}

%0 Conference Paper
%1 KoBrMeBeKoKoRy2016-SE-tradeoffs_perf_prediction_approaches
%A Kounev, Samuel
%A Brosig, Fabian
%A Meier, Philipp
%A Becker, Steffen
%A Koziolek, Anne
%A Koziolek, Heiko
%A Rygielski, Piotr
%B Software Engineering 2016 (SE 2016), Fachtagung des GI-Fachbereichs Softwaretechnik, 23.-26. Feb 2016, Vienna, Austria
%C Vienna, Austria
%D 2016
%I GI
%P 47--48
%T {Analysis of the Trade-offs in Different Modeling Approaches for Performance Prediction of Software Systems}

{Getting the Data}. Happe, Jens; Klatt, Benjamin; K{ü}ster, Martin; Brosig, Fabian; Wert, Alexander; Spinner, Simon; Stier, Christian; Koziolek, Heiko; in {Modeling and Simulating Software Architectures - The Palladio Approach}, R. H. Reussner, S. Becker, J. Happe, R. Heinrich, A. Koziolek, H. Koziolek, M. Kramer, K. Krogmann (eds.) (2016). MIT Press, Cambridge, MA.

In Press

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@incollection{HaKlKuBrWeSpStKo-2016-MITPress-Palladio,
  address = {Cambridge, MA},
  author = {Happe, Jens and Klatt, Benjamin and K{\"u}ster, Martin and Brosig, Fabian and Wert, Alexander and Spinner, Simon and Stier, Christian and Koziolek, Heiko},
  booktitle = {{Modeling and Simulating Software Architectures - The Palladio Approach}},
  editor = {Reussner, Ralf H. and Becker, Steffen and Happe, Jens and Heinrich, Robert and Koziolek, Anne and Koziolek, Heiko and Kramer, Max and Krogmann, Klaus},
  keywords = {t_bookchapter},
  note = {In Press},
  publisher = {MIT Press},
  title = {{Getting the Data}},
  year = 2016
}

%0 Book Section
%1 HaKlKuBrWeSpStKo-2016-MITPress-Palladio
%A Happe, Jens
%A Klatt, Benjamin
%A K{ü}ster, Martin
%A Brosig, Fabian
%A Wert, Alexander
%A Spinner, Simon
%A Stier, Christian
%A Koziolek, Heiko
%B {Modeling and Simulating Software Architectures - The Palladio Approach}
%C Cambridge, MA
%D 2016
%E Reussner, Ralf H.
%E Becker, Steffen
%E Happe, Jens
%E Heinrich, Robert
%E Koziolek, Anne
%E Koziolek, Heiko
%E Kramer, Max
%E Krogmann, Klaus
%I MIT Press
%T {Getting the Data}

{Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-based Architectures}. Brosig, Fabian; Meier, Philipp; Becker, Steffen; Koziolek, Anne; Koziolek, Heiko; Kounev, Samuel; in IEEE Transactions on Software Engineering (TSE) (2015). 41(2) 157–175. IEEE.

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@article{BrMeBeKoKoKo2014-TSE-ModelSolutionTradeOffs,
  author = {Brosig, Fabian and Meier, Philipp and Becker, Steffen and Koziolek, Anne and Koziolek, Heiko and Kounev, Samuel},
  journal = {IEEE Transactions on Software Engineering (TSE)},
  keywords = {QPN},
  month = {{February}},
  number = 2,
  pages = {157–175},
  publisher = {IEEE},
  title = {{Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-based Architectures}},
  volume = 41,
  year = 2015
}

%0 Journal Article
%1 BrMeBeKoKoKo2014-TSE-ModelSolutionTradeOffs
%A Brosig, Fabian
%A Meier, Philipp
%A Becker, Steffen
%A Koziolek, Anne
%A Koziolek, Heiko
%A Kounev, Samuel
%D 2015
%I IEEE
%J IEEE Transactions on Software Engineering (TSE)
%N 2
%P 157--175
%T {Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-based Architectures}
%U http://dx.doi.org/10.1109/TSE.2014.2362755
%V 41

{Evaluating Approaches to Resource Demand Estimation}. Spinner, Simon; Casale, Giuliano; Brosig, Fabian; Kounev, Samuel; in Performance Evaluation (2015). 92 51–71. Elsevier B.V.

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Resource demands are a key parameter of stochastic performance models that needs to be determined when performing a quantitative performance analysis of a system. However, the direct measurement of resource demands is not feasible in most realistic systems. Therefore, statistical approaches that estimate resource demands based on coarse-grained monitoring data (e.g., CPU utilization, and response times) have been proposed in the literature. These approaches have different assumptions and characteristics that need to be considered when estimating resource demands. This paper surveys the state-of-the-art in resource demand estimation and proposes a classification scheme for estimation approaches. Furthermore, it contains an experimental evaluation comparing the impact of different factors (monitoring window size, number of workload classes, load level, collinearity, and model mismatch) on the estimation accuracy of seven different approaches. The classification scheme and the experimental comparison helps performance engineers to select an approach to resource demand estimation that fulfills the requirements of a given analysis scenario.

@article{SpCaBrKo2015-PEVA-ResDemEstSurvey,
  abstract = {Resource demands are a key parameter of stochastic performance models that needs to be determined when performing a quantitative performance analysis of a system. However, the direct measurement of resource demands is not feasible in most realistic systems. Therefore, statistical approaches that estimate resource demands based on coarse-grained monitoring data (e.g., CPU utilization, and response times) have been proposed in the literature. These approaches have different assumptions and characteristics that need to be considered when estimating resource demands. This paper surveys the state-of-the-art in resource demand estimation and proposes a classification scheme for estimation approaches. Furthermore, it contains an experimental evaluation comparing the impact of different factors (monitoring window size, number of workload classes, load level, collinearity, and model mismatch) on the estimation accuracy of seven different approaches. The classification scheme and the experimental comparison helps performance engineers to select an approach to resource demand estimation that fulfills the requirements of a given analysis scenario.},
  author = {Spinner, Simon and Casale, Giuliano and Brosig, Fabian and Kounev, Samuel},
  journal = {Performance Evaluation},
  keywords = {Instrumentation_profiling_and_workload_characterization},
  month = 10,
  pages = {51–71},
  publisher = {Elsevier B.V.},
  title = {{Evaluating Approaches to Resource Demand Estimation}},
  volume = 92,
  year = 2015
}

%0 Journal Article
%1 SpCaBrKo2015-PEVA-ResDemEstSurvey
%A Spinner, Simon
%A Casale, Giuliano
%A Brosig, Fabian
%A Kounev, Samuel
%D 2015
%I Elsevier B.V.
%J Performance Evaluation
%P 51--71
%T {Evaluating Approaches to Resource Demand Estimation}
%U http://www.sciencedirect.com/science/article/pii/S0166531615000711
%V 92
%X Resource demands are a key parameter of stochastic performance models that needs to be determined when performing a quantitative performance analysis of a system. However, the direct measurement of resource demands is not feasible in most realistic systems. Therefore, statistical approaches that estimate resource demands based on coarse-grained monitoring data (e.g., CPU utilization, and response times) have been proposed in the literature. These approaches have different assumptions and characteristics that need to be considered when estimating resource demands. This paper surveys the state-of-the-art in resource demand estimation and proposes a classification scheme for estimation approaches. Furthermore, it contains an experimental evaluation comparing the impact of different factors (monitoring window size, number of workload classes, load level, collinearity, and model mismatch) on the estimation accuracy of seven different approaches. The classification scheme and the experimental comparison helps performance engineers to select an approach to resource demand estimation that fulfills the requirements of a given analysis scenario.

{The Descartes Modeling Language for Self-Aware Performance and Resource Management}. Kounev, Samuel; Brosig, Fabian; Huber, Nikolaus; in Software Engineering 2015, Fachtagung des GI-Fachbereichs Softwaretechnik, 17. - 20. M{ä}rz 2015, Dresden, Deutschland (2015). GI, Bonn, Germany.

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@inproceedings{KoBrHu2015-SE-SelfAwareDescartes,
  address = {Bonn, Germany},
  author = {Kounev, Samuel and Brosig, Fabian and Huber, Nikolaus},
  booktitle = {Software Engineering 2015, Fachtagung des GI-Fachbereichs Softwaretechnik, 17. - 20. M{\"a}rz 2015, Dresden, Deutschland},
  keywords = {Self-aware-computing},
  month = {03},
  publisher = {GI},
  series = {Lecture Notes in Informatics (LNI)},
  title = {{The Descartes Modeling Language for Self-Aware Performance and Resource Management}},
  year = 2015
}

%0 Conference Paper
%1 KoBrHu2015-SE-SelfAwareDescartes
%A Kounev, Samuel
%A Brosig, Fabian
%A Huber, Nikolaus
%B Software Engineering 2015, Fachtagung des GI-Fachbereichs Softwaretechnik, 17. - 20. M{ä}rz 2015, Dresden, Deutschland
%C Bonn, Germany
%D 2015
%I GI
%T {The Descartes Modeling Language for Self-Aware Performance and Resource Management}

{Performance Queries for Architecture-Level Performance Models}. Gorsler, Fabian; Brosig, Fabian; Kounev, Samuel; in Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014) (2014). ACM, New York, NY, USA.

Acceptance Rate (Full Paper): 14/56 = 25\%.

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@inproceedings{GoBrKo2014-PerformanceQueries,
  address = {New York, NY, USA},
  author = {Gorsler, Fabian and Brosig, Fabian and Kounev, Samuel},
  booktitle = {Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)},
  keywords = {DQL},
  month = {{March}},
  note = {Acceptance Rate (Full Paper): 14/56 = 25\%.},
  publisher = {ACM},
  title = {{Performance Queries for Architecture-Level Performance Models}},
  year = 2014
}

%0 Conference Paper
%1 GoBrKo2014-PerformanceQueries
%A Gorsler, Fabian
%A Brosig, Fabian
%A Kounev, Samuel
%B Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)
%C New York, NY, USA
%D 2014
%I ACM
%T {Performance Queries for Architecture-Level Performance Models}

{Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments}. Huber, Nikolaus; van Hoorn, Andr{é}; Koziolek, Anne; Brosig, Fabian; Kounev, Samuel; in Service Oriented Computing and Applications Journal (SOCA) (2014). 8(1) 73–89. Springer-Verlag.

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@article{HuHoKoBrKo2014-SOCA-ModelingRuntimeAdaptation,
  author = {Huber, Nikolaus and van Hoorn, Andr{\'e} and Koziolek, Anne and Brosig, Fabian and Kounev, Samuel},
  journal = {Service Oriented Computing and Applications Journal (SOCA)},
  keywords = {Self-adaptive-systems},
  month = {{March}},
  number = 1,
  pages = {73–89},
  publisher = {Springer-Verlag},
  title = {{Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments}},
  volume = 8,
  year = 2014
}

%0 Journal Article
%1 HuHoKoBrKo2014-SOCA-ModelingRuntimeAdaptation
%A Huber, Nikolaus
%A van Hoorn, Andr{é}
%A Koziolek, Anne
%A Brosig, Fabian
%A Kounev, Samuel
%D 2014
%I Springer-Verlag
%J Service Oriented Computing and Applications Journal (SOCA)
%N 1
%P 73--89
%T {Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments}
%V 8

{The Descartes Modeling Language} Kounev, Samuel; Brosig, Fabian; Huber, Nikolaus; (2014). {Department of Computer Science, University of Wuerzburg}.

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{This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.}

@techreport{KoBrHu2014-TechReport-DML,
  abstract = {{This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.}},
  author = {Kounev, Samuel and Brosig, Fabian and Huber, Nikolaus},
  institution = {{Department of Computer Science, University of Wuerzburg}},
  keywords = {Self-adaptive-systems},
  month = 10,
  title = {{The Descartes Modeling Language}},
  year = 2014
}

%0 Report
%1 KoBrHu2014-TechReport-DML
%A Kounev, Samuel
%A Brosig, Fabian
%A Huber, Nikolaus
%D 2014
%T {The Descartes Modeling Language}
%U http://www.descartes-research.net/dml/
%X {This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.}

{Architecture-Level Software Performance Abstractions for Online Performance Prediction}. Brosig, Fabian; Huber, Nikolaus; Kounev, Samuel; in Elsevier Science of Computer Programming Journal (SciCo) (2014). Vol. 90, Part B 71–92. Elsevier.

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@article{BrHuKo2013-SciCo-SoftwarePerformanceAbstractions,
  author = {Brosig, Fabian and Huber, Nikolaus and Kounev, Samuel},
  journal = {Elsevier Science of Computer Programming Journal (SciCo)},
  keywords = {t_journalmagazine},
  month = {09},
  pages = {71–92},
  publisher = {Elsevier},
  title = {{Architecture-Level Software Performance Abstractions for Online Performance Prediction}},
  volume = {Vol. 90, Part B},
  year = 2014
}

%0 Journal Article
%1 BrHuKo2013-SciCo-SoftwarePerformanceAbstractions
%A Brosig, Fabian
%A Huber, Nikolaus
%A Kounev, Samuel
%D 2014
%I Elsevier
%J Elsevier Science of Computer Programming Journal (SciCo)
%P 71--92
%T {Architecture-Level Software Performance Abstractions for Online Performance Prediction}
%U http://authors.elsevier.com/sd/article/S0167642313001421
%V Vol. 90, Part B

Architecture-Level Software Performance Models for Online Performance Prediction. Brosig, Fabian; (2014, July). Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

{Distinction}

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@phdthesis{Brosig2014-KIT-OnlinePerformancePrediction,
  author = {Brosig, Fabian},
  keywords = {Prediction},
  month = {07},
  note = {<b>{Distinction}</b>},
  school = {Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany},
  title = {Architecture-Level Software Performance Models for Online Performance Prediction},
  year = 2014
}

%0 Thesis
%1 Brosig2014-KIT-OnlinePerformancePrediction
%A Brosig, Fabian
%D 2014
%T Architecture-Level Software Performance Models for Online Performance Prediction
%U http://nbn-resolving.org/urn:nbn:de:swb:90-435372

Controlling the Palladio Bench using the Descartes Query Language. Gorsler, Fabian; Brosig, Fabian; Kounev, Samuel; in Proceedings of the Symposium on Software Performance: Joint Kieker/Palladio Days (KPDAYS 2013), S. Becker, W. Hasselbring, A. van Hoorn, R. Reussner (eds.) (2013). 109–118. CEUR-WS.org, Aachen, Germany.

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The Palladio Bench is a tool to model, simulate and analyze Palladio Component Model (PCM) instances. However, for the Palladio Bench, no single interface to automate experiments or Application Programming Interface (API) to trigger the simulation of PCM instances and to extract performance prediction results is available. The Descartes Query Language (DQL) is a novel approach of a declarative query language to integrate different performance modeling and prediction techniques behind a unifying interface. Users benefit from the abstraction of specific tools to prepare and trigger performance predictions, less effort to obtain performance metrics of interest, and means to automate performance predictions. In this paper, we describe the realization of a DQL Connector for PCM and demonstrate the applicability of our approach in a case study.

@inproceedings{GoBrKo2013-ControllingPCM,
  abstract = {The Palladio Bench is a tool to model, simulate and analyze Palladio Component Model (PCM) instances. However, for the Palladio Bench, no single interface to automate experiments or Application Programming Interface (API) to trigger the simulation of PCM instances and to extract performance prediction results is available. The Descartes Query Language (DQL) is a novel approach of a declarative query language to integrate different performance modeling and prediction techniques behind a unifying interface. Users benefit from the abstraction of specific tools to prepare and trigger performance predictions, less effort to obtain performance metrics of interest, and means to automate performance predictions. In this paper, we describe the realization of a DQL Connector for PCM and demonstrate the applicability of our approach in a case study.},
  address = {Aachen, Germany},
  author = {Gorsler, Fabian and Brosig, Fabian and Kounev, Samuel},
  booktitle = {Proceedings of the Symposium on Software Performance: Joint Kieker/Palladio Days (KPDAYS 2013)},
  editor = {Becker, Steffen and Hasselbring, Wilhelm and van Hoorn, Andr{\'e} and Reussner, Ralf},
  keywords = {Optimization},
  month = 11,
  number = 1083,
  pages = {109–118},
  publisher = {CEUR-WS.org},
  title = {Controlling the Palladio Bench using the Descartes Query Language},
  year = 2013
}

%0 Conference Paper
%1 GoBrKo2013-ControllingPCM
%A Gorsler, Fabian
%A Brosig, Fabian
%A Kounev, Samuel
%B Proceedings of the Symposium on Software Performance: Joint Kieker/Palladio Days (KPDAYS 2013)
%C Aachen, Germany
%D 2013
%E Becker, Steffen
%E Hasselbring, Wilhelm
%E van Hoorn, Andr{é}
%E Reussner, Ralf
%I CEUR-WS.org
%N 1083
%P 109--118
%T Controlling the Palladio Bench using the Descartes Query Language
%U http://ceur-ws.org/Vol-1083/
%X The Palladio Bench is a tool to model, simulate and analyze Palladio Component Model (PCM) instances. However, for the Palladio Bench, no single interface to automate experiments or Application Programming Interface (API) to trigger the simulation of PCM instances and to extract performance prediction results is available. The Descartes Query Language (DQL) is a novel approach of a declarative query language to integrate different performance modeling and prediction techniques behind a unifying interface. Users benefit from the abstraction of specific tools to prepare and trigger performance predictions, less effort to obtain performance metrics of interest, and means to automate performance predictions. In this paper, we describe the realization of a DQL Connector for PCM and demonstrate the applicability of our approach in a case study.

{Evaluating Approaches for Performance Prediction in Virtualized Environments}. Brosig, Fabian; Gorsler, Fabian; Huber, Nikolaus; Kounev, Samuel; in Proceedings of the IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2013) (2013).

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@inproceedings{BrGoHuKo2013-MASCOTS-EvaluationApproachesForPerformancePredictionInVirtualizedEnvironments,
  author = {Brosig, Fabian and Gorsler, Fabian and Huber, Nikolaus and Kounev, Samuel},
  booktitle = {Proceedings of the IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2013)},
  keywords = {Virtualization},
  month = {08},
  title = {{Evaluating Approaches for Performance Prediction in Virtualized Environments}},
  year = 2013
}

%0 Conference Paper
%1 BrGoHuKo2013-MASCOTS-EvaluationApproachesForPerformancePredictionInVirtualizedEnvironments
%A Brosig, Fabian
%A Gorsler, Fabian
%A Huber, Nikolaus
%A Kounev, Samuel
%B Proceedings of the IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2013)
%D 2013
%T {Evaluating Approaches for Performance Prediction in Virtualized Environments}

{Towards Truthful Resource Reservation in Cloud Computing}. Funke, Daniel; Brosig, Fabian; Faber, Michael; in {Proceedings of the 6th International ICST Conference on Performance Evaluation Methodologies and Tools (ValueTools 2012), Carg{`e}se, France} (2012).

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{Prudent capacity planning to meet their clients future computational needs is one of the major issues cloud computing providers face today. By offering resource reservations in advance, providers gain insight into the projected demand of their customers and can act accordingly. However, customers need to be given an incentive, e.g. discounts granted, to commit early to a provider and to honestly, i.e., truthfully reserve their predicted future resource requirements. Customers may reserve capacity deviating from their truly predicted demand, in order to exploit the mechanism for their own benefit, thereby causing futile costs for the provider. In this paper we prove, using a game theoretic approach, that truthful reservation is the best, i.e., dominant strategy for customers if they are capable to make precise forecasts of their demands and that deviations from truth-telling can be profitable for customers if their demand forecasts are uncertain.}

@inproceedings{FuBrFa2012-VALUETOOLS-TruthfulResourceReservation,
  abstract = {{Prudent capacity planning to meet their clients future computational needs is one of the major issues cloud computing providers face today. By offering resource reservations in advance, providers gain insight into the projected demand of their customers and can act accordingly. However, customers need to be given an incentive, e.g. discounts granted, to commit early to a provider and to honestly, i.e., truthfully reserve their predicted future resource requirements. Customers may reserve capacity deviating from their truly predicted demand, in order to exploit the mechanism for their own benefit, thereby causing futile costs for the provider. In this paper we prove, using a game theoretic approach, that truthful reservation is the best, i.e., dominant strategy for customers if they are capable to make precise forecasts of their demands and that deviations from truth-telling can be profitable for customers if their demand forecasts are uncertain.}},
  author = {Funke, Daniel and Brosig, Fabian and Faber, Michael},
  booktitle = {{Proceedings of the 6th International ICST Conference on Performance Evaluation Methodologies and Tools (ValueTools 2012), Carg{`e}se, France}},
  keywords = {Optimization},
  month = 10,
  title = {{Towards Truthful Resource Reservation in Cloud Computing}},
  year = 2012
}

%0 Conference Paper
%1 FuBrFa2012-VALUETOOLS-TruthfulResourceReservation
%A Funke, Daniel
%A Brosig, Fabian
%A Faber, Michael
%B {Proceedings of the 6th International ICST Conference on Performance Evaluation Methodologies and Tools (ValueTools 2012), Carg{`e}se, France}
%D 2012
%T {Towards Truthful Resource Reservation in Cloud Computing}
%X {Prudent capacity planning to meet their clients future computational needs is one of the major issues cloud computing providers face today. By offering resource reservations in advance, providers gain insight into the projected demand of their customers and can act accordingly. However, customers need to be given an incentive, e.g. discounts granted, to commit early to a provider and to honestly, i.e., truthfully reserve their predicted future resource requirements. Customers may reserve capacity deviating from their truly predicted demand, in order to exploit the mechanism for their own benefit, thereby causing futile costs for the provider. In this paper we prove, using a game theoretic approach, that truthful reservation is the best, i.e., dominant strategy for customers if they are capable to make precise forecasts of their demands and that deviations from truth-telling can be profitable for customers if their demand forecasts are uncertain.}

{S/T/A: Meta-Modeling Run-Time Adaptation in Component-Based System Architectures}. Huber, Nikolaus; van Hoorn, Andr{é}; Koziolek, Anne; Brosig, Fabian; Kounev, Samuel; in Proceedings of the 9th IEEE International Conference on e-Business Engineering (ICEBE 2012) (2012). 70–77. IEEE Computer Society, Los Alamitos, CA, USA.

Acceptance Rate (Full Paper): 19.7\% (26/132)

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Modern virtualized system environments usually host diverse applications of different parties and aim at utilizing resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system specific, it is a challenge to abstract from system details enabling the reuse of adaptation strategies. In this paper, we propose a novel modeling language (meta-model) providing means to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to three different realistic contexts (dynamic resource allocation, software architecture optimization, and run-time adaptation planning) showing how the gap between complex manual adaptations and their autonomous execution can be closed by using a holistic model-based approach.

@inproceedings{HuHoKoBrKo2012-ICEBE-STA,
  abstract = {Modern virtualized system environments usually host diverse applications of different parties and aim at utilizing resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system specific, it is a challenge to abstract from system details enabling the reuse of adaptation strategies. In this paper, we propose a novel modeling language (meta-model) providing means to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to three different realistic contexts (dynamic resource allocation, software architecture optimization, and run-time adaptation planning) showing how the gap between complex manual adaptations and their autonomous execution can be closed by using a holistic model-based approach.},
  address = {Los Alamitos, CA, USA},
  author = {Huber, Nikolaus and van Hoorn, Andr{\'e} and Koziolek, Anne and Brosig, Fabian and Kounev, Samuel},
  booktitle = {Proceedings of the 9th IEEE International Conference on e-Business Engineering (ICEBE 2012)},
  keywords = {Optimization},
  month = {09},
  note = {Acceptance Rate (Full Paper): 19.7\% (26/132)},
  pages = {70–77},
  publisher = {IEEE Computer Society},
  title = {{S/T/A: Meta-Modeling Run-Time Adaptation in Component-Based System Architectures}},
  year = 2012
}

%0 Conference Paper
%1 HuHoKoBrKo2012-ICEBE-STA
%A Huber, Nikolaus
%A van Hoorn, Andr{é}
%A Koziolek, Anne
%A Brosig, Fabian
%A Kounev, Samuel
%B Proceedings of the 9th IEEE International Conference on e-Business Engineering (ICEBE 2012)
%C Los Alamitos, CA, USA
%D 2012
%I IEEE Computer Society
%P 70--77
%T {S/T/A: Meta-Modeling Run-Time Adaptation in Component-Based System Architectures}
%U http://conferences.computer.org/icebe/2012/index.htm
%X Modern virtualized system environments usually host diverse applications of different parties and aim at utilizing resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system specific, it is a challenge to abstract from system details enabling the reuse of adaptation strategies. In this paper, we propose a novel modeling language (meta-model) providing means to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to three different realistic contexts (dynamic resource allocation, software architecture optimization, and run-time adaptation planning) showing how the gap between complex manual adaptations and their autonomous execution can be closed by using a holistic model-based approach.

{Modeling Dynamic Virtualized Resource Landscapes}. Huber, Nikolaus; Brosig, Fabian; Kounev, Samuel; in Proceedings of the 8th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2012) (2012). 81–90. ACM, New York, NY, USA.

Acceptance Rate (Full Paper): 25.6\%

[ Abstract ]
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Modern data centers are subject to an increasing demand for flexibility. Increased flexibility and dynamics, however, also result in a higher system complexity. This complexity carries on to run-time resource management for Quality-of-Service (QoS) enforcement, rendering design-time approaches for QoS assurance inadequate. In this paper, we present a set of novel meta-models that can be used to describe the resource landscape, the architecture and resource layers of dynamic virtualized data center infrastructures, as well as their run-time adaptation and resource management aspects. With these meta-models we introduce new modeling concepts to improve model-based run-time QoS assurance. We evaluate our meta-models by modeling a representative virtualized service infrastructure and using these model instances for run-time resource allocation. The results demonstrate the benefits of the new meta-models and show how they can be used to improve model-based system adaptation and run-time resource management in dynamic virtualized data centers.

@inproceedings{HuBrKo2012-QoSA-ModelingVirtResLandscapes,
  abstract = {Modern data centers are subject to an increasing demand for flexibility. Increased flexibility and dynamics, however, also result in a higher system complexity. This complexity carries on to run-time resource management for Quality-of-Service (QoS) enforcement, rendering design-time approaches for QoS assurance inadequate. In this paper, we present a set of novel meta-models that can be used to describe the resource landscape, the architecture and resource layers of dynamic virtualized data center infrastructures, as well as their run-time adaptation and resource management aspects. With these meta-models we introduce new modeling concepts to improve model-based run-time QoS assurance. We evaluate our meta-models by modeling a representative virtualized service infrastructure and using these model instances for run-time resource allocation. The results demonstrate the benefits of the new meta-models and show how they can be used to improve model-based system adaptation and run-time resource management in dynamic virtualized data centers.},
  address = {New York, NY, USA},
  author = {Huber, Nikolaus and Brosig, Fabian and Kounev, Samuel},
  booktitle = {Proceedings of the 8th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2012)},
  keywords = {Self-adaptive-systems},
  month = {06},
  note = {Acceptance Rate (Full Paper): 25.6\%},
  pages = {81–90},
  publisher = {ACM},
  title = {{Modeling Dynamic Virtualized Resource Landscapes}},
  year = 2012
}

%0 Conference Paper
%1 HuBrKo2012-QoSA-ModelingVirtResLandscapes
%A Huber, Nikolaus
%A Brosig, Fabian
%A Kounev, Samuel
%B Proceedings of the 8th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2012)
%C New York, NY, USA
%D 2012
%I ACM
%P 81--90
%T {Modeling Dynamic Virtualized Resource Landscapes}
%U http://qosa.ipd.kit.edu/qosa_2012/
%X Modern data centers are subject to an increasing demand for flexibility. Increased flexibility and dynamics, however, also result in a higher system complexity. This complexity carries on to run-time resource management for Quality-of-Service (QoS) enforcement, rendering design-time approaches for QoS assurance inadequate. In this paper, we present a set of novel meta-models that can be used to describe the resource landscape, the architecture and resource layers of dynamic virtualized data center infrastructures, as well as their run-time adaptation and resource management aspects. With these meta-models we introduce new modeling concepts to improve model-based run-time QoS assurance. We evaluate our meta-models by modeling a representative virtualized service infrastructure and using these model instances for run-time resource allocation. The results demonstrate the benefits of the new meta-models and show how they can be used to improve model-based system adaptation and run-time resource management in dynamic virtualized data centers.

{Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models}. Brosig, Fabian; Huber, Nikolaus; Kounev, Samuel; in Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26--28, 2012, Bertinoro, Italy (2012).

Acceptance Rate (Full Paper): 28.5\%.

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Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.

@inproceedings{BrHuKo2012-CBSE-ParamAndContextDep,
  abstract = {Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.},
  author = {Brosig, Fabian and Huber, Nikolaus and Kounev, Samuel},
  booktitle = {Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26–28, 2012, Bertinoro, Italy},
  keywords = {Application_quality_of_service_management},
  month = {06},
  note = {Acceptance Rate (Full Paper): 28.5\%.},
  title = {{Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models}},
  year = 2012
}

%0 Conference Paper
%1 BrHuKo2012-CBSE-ParamAndContextDep
%A Brosig, Fabian
%A Huber, Nikolaus
%A Kounev, Samuel
%B Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26--28, 2012, Bertinoro, Italy
%D 2012
%T {Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models}
%U http://cbse-conferences.org/2012/
%X Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.

{A Method for Experimental Analysis and Modeling of Virtualization Performance Overhead}. Huber, Nikolaus; von Quast, Marcel; Brosig, Fabian; Hauck, Michael; Kounev, Samuel; in Cloud Computing and Services Science, I. Ivanov, M. van Sinderen, B. Shishkov (eds.) (2012). 353–370. Springer, New York.

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@incollection{HuQuBrHaKo2012-CCaSS-ExpVirtPerfOverhead,
  address = {New York},
  author = {Huber, Nikolaus and von Quast, Marcel and Brosig, Fabian and Hauck, Michael and Kounev, Samuel},
  booktitle = {Cloud Computing and Services Science},
  editor = {Ivanov, Ivan and van Sinderen, Marten and Shishkov, Boris},
  keywords = {t_bookchapter},
  pages = {353–370},
  publisher = {Springer},
  series = {Service Science: Research and Innovations in the Service Economy},
  title = {{A Method for Experimental Analysis and Modeling of Virtualization Performance Overhead}},
  year = 2012
}

%0 Book Section
%1 HuQuBrHaKo2012-CCaSS-ExpVirtPerfOverhead
%A Huber, Nikolaus
%A von Quast, Marcel
%A Brosig, Fabian
%A Hauck, Michael
%A Kounev, Samuel
%B Cloud Computing and Services Science
%C New York
%D 2012
%E Ivanov, Ivan
%E van Sinderen, Marten
%E Shishkov, Boris
%I Springer
%P 353--370
%T {A Method for Experimental Analysis and Modeling of Virtualization Performance Overhead}
%U http://dx.doi.org/10.1007/978-1-4614-2326-3_19

Online prediction: Four case studies. Gilly, Katja; Brosig, Fabian; Nou, Ramon; Kounev, Samuel; Juiz, Carlos; in Resilience Assessment and Evaluation of Computing Systems, K. Wolter, A. Avritzer, M. Vieira, A. van Moorsel (eds.) (2012). Springer-Verlag, Berlin, Heidelberg.

ISBN: 978-3-642-29031-2

[ Abstract ]
[ BibTeX ]
[ EndNote ]
[ URL ]
[ Download ]

{Current computing systems are becoming increasingly complex in nature and exhibit large variations in workloads. These changing environments create challenges to the design of systems that can adapt themselves while maintaining desired Quality of Service (QoS), security, dependability, availability and other non-functional requirements. The next generation of resilient systems will be highly distributed, component-based and service-oriented. They will need to operate in unattended mode and possibly in hostile environments, will be composed of a large number of interchangeable components discoverable at run-time, and will have to run on a multitude of unknown and heterogeneous hardware and network platforms. These computer systems will adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and software failures, load variation and variations in user interaction with the system, including security attacks and overwhelming situations. This self adaptation of next resilient systems can be achieved by first online predicting how these situations would be by observation of the current environment. This chapter focuses on the use of online predicting methods, techniques and tools for resilient systems. Thus, we survey online QoS adaptive models in several environments as grid environments, service-oriented architectures and ambient intelligence using different approaches based on queueing networks, model checking, ontology engineering among others.}

@incollection{GiBrNoKoJu2012-ResBook-OnlinePrediction,
  abstract = {{Current computing systems are becoming increasingly complex in nature and exhibit large variations in workloads. These changing environments create challenges to the design of systems that can adapt themselves while maintaining desired Quality of Service (QoS), security, dependability, availability and other non-functional requirements. The next generation of resilient systems will be highly distributed, component-based and service-oriented. They will need to operate in unattended mode and possibly in hostile environments, will be composed of a large number of interchangeable components discoverable at run-time, and will have to run on a multitude of unknown and heterogeneous hardware and network platforms. These computer systems will adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and software failures, load variation and variations in user interaction with the system, including security attacks and overwhelming situations. This self adaptation of next resilient systems can be achieved by first online predicting how these situations would be by observation of the current environment. This chapter focuses on the use of online predicting methods, techniques and tools for resilient systems. Thus, we survey online QoS adaptive models in several environments as grid environments, service-oriented architectures and ambient intelligence using different approaches based on queueing networks, model checking, ontology engineering among others.}},
  address = {Berlin, Heidelberg},
  author = {Gilly, Katja and Brosig, Fabian and Nou, Ramon and Kounev, Samuel and Juiz, Carlos},
  booktitle = {Resilience Assessment and Evaluation of Computing Systems},
  editor = {Wolter, K. and Avritzer, A. and Vieira, M. and van Moorsel, A.},
  keywords = {Reliability},
  note = {ISBN: 978-3-642-29031-2},
  publisher = {Springer-Verlag},
  series = {XVIII},
  title = {Online prediction: Four case studies},
  year = 2012
}

%0 Book Section
%1 GiBrNoKoJu2012-ResBook-OnlinePrediction
%A Gilly, Katja
%A Brosig, Fabian
%A Nou, Ramon
%A Kounev, Samuel
%A Juiz, Carlos
%B Resilience Assessment and Evaluation of Computing Systems
%C Berlin, Heidelberg
%D 2012
%E Wolter, K.
%E Avritzer, A.
%E Vieira, M.
%E van Moorsel, A.
%I Springer-Verlag
%T Online prediction: Four case studies
%U http://www.springer.com/computer/communication+networks/book/978-3-642-29031-2
%X {Current computing systems are becoming increasingly complex in nature and exhibit large variations in workloads. These changing environments create challenges to the design of systems that can adapt themselves while maintaining desired Quality of Service (QoS), security, dependability, availability and other non-functional requirements. The next generation of resilient systems will be highly distributed, component-based and service-oriented. They will need to operate in unattended mode and possibly in hostile environments, will be composed of a large number of interchangeable components discoverable at run-time, and will have to run on a multitude of unknown and heterogeneous hardware and network platforms. These computer systems will adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and software failures, load variation and variations in user interaction with the system, including security attacks and overwhelming situations. This self adaptation of next resilient systems can be achieved by first online predicting how these situations would be by observation of the current environment. This chapter focuses on the use of online predicting methods, techniques and tools for resilient systems. Thus, we survey online QoS adaptive models in several environments as grid environments, service-oriented architectures and ambient intelligence using different approaches based on queueing networks, model checking, ontology engineering among others.}

Providing Dependability and Resilience in the Cloud: Challenges and Opportunities. Kounev, Samuel; Reinecke, Philipp; Brosig, Fabian; Bradley, Jeremy T.; Joshi, Kaustubh; Babka, Vlastimil; Stefanek, Anton; Gilmore, Stephen; in Resilience Assessment and Evaluation of Computing Systems, K. Wolter, A. Avritzer, M. Vieira, A. van Moorsel (eds.) (2012). Springer-Verlag, Berlin, Heidelberg.

ISBN: 978-3-642-29031-2

[ Abstract ]
[ BibTeX ]
[ EndNote ]
[ URL ]
[ Download ]

{Cloud Computing is a novel paradigm for providing data center resources as on demand services in a pay-as-you-go manner. It promises significant cost savings by making it possible to consolidate workloads and share infrastructure resources among multiple applications resulting in higher cost- and energy-efficiency. However, these benefits come at the cost of increased system complexity and dynamicity posing new challenges in providing service dependability and resilience for applications running in a Cloud environment. At the same time, the virtualization of physical resources, inherent in Cloud Computing, provides new opportunities for novel dependability and quality-of-service management techniques that can potentially improve system resilience. In this chapter, we first discuss in detail the challenges and opportunities introduced by the Cloud Computing paradigm. We then provide a review of the state-of-the-art on dependability and resilience management in Cloud environments, and conclude with an overview of emerging research directions.}

@incollection{KoReBrBrJoBaStGi2012-ResBook-CloudChallenges,
  abstract = {{Cloud Computing is a novel paradigm for providing data center resources as on demand services in a pay-as-you-go manner. It promises significant cost savings by making it possible to consolidate workloads and share infrastructure resources among multiple applications resulting in higher cost- and energy-efficiency. However, these benefits come at the cost of increased system complexity and dynamicity posing new challenges in providing service dependability and resilience for applications running in a Cloud environment. At the same time, the virtualization of physical resources, inherent in Cloud Computing, provides new opportunities for novel dependability and quality-of-service management techniques that can potentially improve system resilience. In this chapter, we first discuss in detail the challenges and opportunities introduced by the Cloud Computing paradigm. We then provide a review of the state-of-the-art on dependability and resilience management in Cloud environments, and conclude with an overview of emerging research directions.}},
  address = {Berlin, Heidelberg},
  author = {Kounev, Samuel and Reinecke, Philipp and Brosig, Fabian and Bradley, Jeremy T. and Joshi, Kaustubh and Babka, Vlastimil and Stefanek, Anton and Gilmore, Stephen},
  booktitle = {Resilience Assessment and Evaluation of Computing Systems},
  editor = {Wolter, K. and Avritzer, A. and Vieira, M. and van Moorsel, A.},
  keywords = {Power},
  note = {ISBN: 978-3-642-29031-2},
  publisher = {Springer-Verlag},
  series = {XVIII},
  title = {Providing Dependability and Resilience in the Cloud: Challenges and Opportunities},
  year = 2012
}

%0 Book Section
%1 KoReBrBrJoBaStGi2012-ResBook-CloudChallenges
%A Kounev, Samuel
%A Reinecke, Philipp
%A Brosig, Fabian
%A Bradley, Jeremy T.
%A Joshi, Kaustubh
%A Babka, Vlastimil
%A Stefanek, Anton
%A Gilmore, Stephen
%B Resilience Assessment and Evaluation of Computing Systems
%C Berlin, Heidelberg
%D 2012
%E Wolter, K.
%E Avritzer, A.
%E Vieira, M.
%E van Moorsel, A.
%I Springer-Verlag
%T Providing Dependability and Resilience in the Cloud: Challenges and Opportunities
%U http://www.springer.com/computer/communication+networks/book/978-3-642-29031-2
%X {Cloud Computing is a novel paradigm for providing data center resources as on demand services in a pay-as-you-go manner. It promises significant cost savings by making it possible to consolidate workloads and share infrastructure resources among multiple applications resulting in higher cost- and energy-efficiency. However, these benefits come at the cost of increased system complexity and dynamicity posing new challenges in providing service dependability and resilience for applications running in a Cloud environment. At the same time, the virtualization of physical resources, inherent in Cloud Computing, provides new opportunities for novel dependability and quality-of-service management techniques that can potentially improve system resilience. In this chapter, we first discuss in detail the challenges and opportunities introduced by the Cloud Computing paradigm. We then provide a review of the state-of-the-art on dependability and resilience management in Cloud environments, and conclude with an overview of emerging research directions.}

{Providing Dependability and Performance in the Cloud: Case Studies}. Huber, Nikolaus; Brosig, Fabian; Dingle, N.; Joshi, K.; Kounev, Samuel; in {Resilience Assessment and Evaluation of Computing Systems}, K. Wolter, A. Avritzer, M. Vieira, A. van Moorsel (eds.) (2012). Springer-Verlag, Berlin, Heidelberg.

ISBN: 978-3-642-29031-2

[ BibTeX ]
[ EndNote ]
[ URL ]
[ Download ]

@incollection{HuBrDiJoKo2012-ResBook-CloudCaseStudies,
  address = {Berlin, Heidelberg},
  author = {Huber, Nikolaus and Brosig, Fabian and Dingle, N. and Joshi, K. and Kounev, Samuel},
  booktitle = {{Resilience Assessment and Evaluation of Computing Systems}},
  editor = {Wolter, K. and Avritzer, A. and Vieira, M. and van Moorsel, A.},
  keywords = {Optimization},
  note = {ISBN: 978-3-642-29031-2},
  publisher = {Springer-Verlag},
  series = {XVIII},
  title = {{Providing Dependability and Performance in the Cloud: Case Studies}},
  year = 2012
}

%0 Book Section
%1 HuBrDiJoKo2012-ResBook-CloudCaseStudies
%A Huber, Nikolaus
%A Brosig, Fabian
%A Dingle, N.
%A Joshi, K.
%A Kounev, Samuel
%B {Resilience Assessment and Evaluation of Computing Systems}
%C Berlin, Heidelberg
%D 2012
%E Wolter, K.
%E Avritzer, A.
%E Vieira, M.
%E van Moorsel, A.
%I Springer-Verlag
%T {Providing Dependability and Performance in the Cloud: Case Studies}
%U http://www.springer.com/computer/communication+networks/book/978-3-642-29031-2

Model-based Techniques for Performance Engineering of Business Information Systems. Kounev, Samuel; Huber, Nikolaus; Spinner, Simon; Brosig, Fabian; in Business Modeling and Software Design, B. Shishkov (ed.) (2012). (Vol. 0109) 19–37. Springer-Verlag, Berlin, Heidelberg.

[ Abstract ]
[ BibTeX ]
[ EndNote ]
[ URL ]
[ Download ]

{With the increasing adoption of virtualization and the transition towards Cloud Computing platforms, modern business information systems are becoming increasingly complex and dynamic. This raises the challenge of guaranteeing system performance and scalability while at the same time ensuring efficient resource usage. In this paper, we present a historical perspective on the evolution of model-based performance engineering techniques for business information systems focusing on the major developments over the past several decades that have shaped the field. We survey the state-of-the-art on performance modeling and management approaches discussing the ongoing efforts in the community to increasingly bridge the gap between high-level business services and low level performance models. Finally, we wrap up with an outlook on the emergence of self-aware systems engineering as a new research area at the intersection of several computer science disciplines.}

@incollection{KoHuSpBr2012-business-inf-sys,
  abstract = {{With the increasing adoption of virtualization and the transition towards Cloud Computing platforms, modern business information systems are becoming increasingly complex and dynamic. This raises the challenge of guaranteeing system performance and scalability while at the same time ensuring efficient resource usage. In this paper, we present a historical perspective on the evolution of model-based performance engineering techniques for business information systems focusing on the major developments over the past several decades that have shaped the field. We survey the state-of-the-art on performance modeling and management approaches discussing the ongoing efforts in the community to increasingly bridge the gap between high-level business services and low level performance models. Finally, we wrap up with an outlook on the emergence of self-aware systems engineering as a new research area at the intersection of several computer science disciplines.}},
  address = {Berlin, Heidelberg},
  author = {Kounev, Samuel and Huber, Nikolaus and Spinner, Simon and Brosig, Fabian},
  booktitle = {Business Modeling and Software Design},
  editor = {Shishkov, Boris},
  keywords = {t_bookchapter},
  pages = {19–37},
  publisher = {Springer-Verlag},
  series = {Lecture Notes in Business Information Processing (LNBIP)},
  title = {Model-based Techniques for Performance Engineering of Business Information Systems},
  volume = {0109},
  year = 2012
}

%0 Book Section
%1 KoHuSpBr2012-business-inf-sys
%A Kounev, Samuel
%A Huber, Nikolaus
%A Spinner, Simon
%A Brosig, Fabian
%B Business Modeling and Software Design
%C Berlin, Heidelberg
%D 2012
%E Shishkov, Boris
%I Springer-Verlag
%P 19--37
%T Model-based Techniques for Performance Engineering of Business Information Systems
%U http://dx.doi.org/10.1007/978-3-642-29788-5_2
%V 0109
%X {With the increasing adoption of virtualization and the transition towards Cloud Computing platforms, modern business information systems are becoming increasingly complex and dynamic. This raises the challenge of guaranteeing system performance and scalability while at the same time ensuring efficient resource usage. In this paper, we present a historical perspective on the evolution of model-based performance engineering techniques for business information systems focusing on the major developments over the past several decades that have shaped the field. We survey the state-of-the-art on performance modeling and management approaches discussing the ongoing efforts in the community to increasingly bridge the gap between high-level business services and low level performance models. Finally, we wrap up with an outlook on the emergence of self-aware systems engineering as a new research area at the intersection of several computer science disciplines.}

{A}utomated {E}xtraction of {A}rchitecture-{L}evel {P}erformance {M}odels of {D}istributed {C}omponent-{B}ased {S}ystems. Brosig, Fabian; Huber, Nikolaus; Kounev, Samuel; in 26th IEEE/ACM International Conference On Automated Software Engineering (ASE 2011) (2011). Oread, Lawrence, Kansas.

Acceptance Rate (Full Paper): 14.7\% (37/252)

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Modern service-oriented enterprise systems have increasingly complex and dynamic loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment and adapt the system configuration accordingly. Architecture-level performance models provide a powerful tool for performance prediction, however, current approaches to modeling the execution context of software components are not suitable for use at run-time. In this paper, we analyze the typical online performance prediction scenarios and propose a novel performance meta-model for expressing and resolving parameter and context dependencies, specifically designed for use in online scenarios. We motivate and validate our approach in the context of a realistic and representative online performance prediction scenario based on the SPECjEnterprise2010 standard benchmark.

@inproceedings{BrHuKo2011-ASE-AutomExtraction,
  abstract = {Modern service-oriented enterprise systems have increasingly complex and dynamic loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment and adapt the system configuration accordingly. Architecture-level performance models provide a powerful tool for performance prediction, however, current approaches to modeling the execution context of software components are not suitable for use at run-time. In this paper, we analyze the typical online performance prediction scenarios and propose a novel performance meta-model for expressing and resolving parameter and context dependencies, specifically designed for use in online scenarios. We motivate and validate our approach in the context of a realistic and representative online performance prediction scenario based on the SPECjEnterprise2010 standard benchmark.},
  address = {Oread, Lawrence, Kansas},
  author = {Brosig, Fabian and Huber, Nikolaus and Kounev, Samuel},
  booktitle = {26th IEEE/ACM International Conference On Automated Software Engineering (ASE 2011)},
  keywords = {Instrumentation_profiling_and_workload_characterization},
  month = 11,
  note = {Acceptance Rate (Full Paper): 14.7\% (37/252)},
  title = {{A}utomated {E}xtraction of {A}rchitecture-{L}evel {P}erformance {M}odels of {D}istributed {C}omponent-{B}ased {S}ystems},
  year = 2011
}

%0 Conference Paper
%1 BrHuKo2011-ASE-AutomExtraction
%A Brosig, Fabian
%A Huber, Nikolaus
%A Kounev, Samuel
%B 26th IEEE/ACM International Conference On Automated Software Engineering (ASE 2011)
%C Oread, Lawrence, Kansas
%D 2011
%T {A}utomated {E}xtraction of {A}rchitecture-{L}evel {P}erformance {M}odels of {D}istributed {C}omponent-{B}ased {S}ystems
%X Modern service-oriented enterprise systems have increasingly complex and dynamic loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment and adapt the system configuration accordingly. Architecture-level performance models provide a powerful tool for performance prediction, however, current approaches to modeling the execution context of software components are not suitable for use at run-time. In this paper, we analyze the typical online performance prediction scenarios and propose a novel performance meta-model for expressing and resolving parameter and context dependencies, specifically designed for use in online scenarios. We motivate and validate our approach in the context of a realistic and representative online performance prediction scenario based on the SPECjEnterprise2010 standard benchmark.

Fifth International Workshop on the Practical Application of Stochastic Modelling (PASM) Brosig, Fabian; in Electronic Notes in Theoretical Computer Science, (N. Thomas; J. Bradley; W. Knottenbelt; S. Kounev; N. Huber; F. Brosig, eds.) (2011). (Vol. 275) 1–174. Elsevier Science Publishers B. V., Amsterdam, The Netherlands.

[ BibTeX ]
[ EndNote ]

@proceedings{Thomas20111,
  address = {Amsterdam, The Netherlands},
  author = {Brosig, Fabian},
  editor = {Thomas, Nigel and Bradley, Jeremy and Knottenbelt, William and Kounev, Samuel and Huber, Nikolaus and Brosig, Fabian},
  keywords = {Reliability},
  month = {09},
  pages = {1–174},
  publisher = {Elsevier Science Publishers B. V.},
  series = {Electronic Notes in Theoretical Computer Science},
  title = {Fifth International Workshop on the Practical Application of Stochastic Modelling (PASM)},
  volume = 275,
  year = 2011
}

%0 Conference Proceedings
%1 Thomas20111
%A Brosig, Fabian
%B Electronic Notes in Theoretical Computer Science
%C Amsterdam, The Netherlands
%D 2011
%E Thomas, Nigel
%E Bradley, Jeremy
%E Knottenbelt, William
%E Kounev, Samuel
%E Huber, Nikolaus
%E Brosig, Fabian
%I Elsevier Science Publishers B. V.
%P 1--174
%T Fifth International Workshop on the Practical Application of Stochastic Modelling (PASM)
%V 275

{Self-Aware QoS Management in Virtualized Infrastructures}. Kounev, Samuel; Brosig, Fabian; Huber, Nikolaus; in 8th International Conference on Autonomic Computing (ICAC 2011) (2011).

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We present an overview of our work-in-progress and long-term research agenda aiming to develop a novel methodology for engineering of self-aware software systems. The latter will have built-in architecture-level QoS models enhanced to capture dynamic aspects of the system environment and maintained automatically during operation. The models will be exploited at run-time to adapt the system to changes in the environment ensuring that resources are utilized efficiently and QoS requirements are satisfied.

@inproceedings{KoBrHu2011-ICAC-QoSManagement,
  abstract = {We present an overview of our work-in-progress and long-term research agenda aiming to develop a novel methodology for engineering of self-aware software systems. The latter will have built-in architecture-level QoS models enhanced to capture dynamic aspects of the system environment and maintained automatically during operation. The models will be exploited at run-time to adapt the system to changes in the environment ensuring that resources are utilized efficiently and QoS requirements are satisfied.},
  author = {Kounev, Samuel and Brosig, Fabian and Huber, Nikolaus},
  booktitle = {8th International Conference on Autonomic Computing (ICAC 2011)},
  keywords = {Reliability},
  month = {06},
  title = {{Self-Aware QoS Management in Virtualized Infrastructures}},
  year = 2011
}

%0 Conference Paper
%1 KoBrHu2011-ICAC-QoSManagement
%A Kounev, Samuel
%A Brosig, Fabian
%A Huber, Nikolaus
%B 8th International Conference on Autonomic Computing (ICAC 2011)
%D 2011
%T {Self-Aware QoS Management in Virtualized Infrastructures}
%X We present an overview of our work-in-progress and long-term research agenda aiming to develop a novel methodology for engineering of self-aware software systems. The latter will have built-in architecture-level QoS models enhanced to capture dynamic aspects of the system environment and maintained automatically during operation. The models will be exploited at run-time to adapt the system to changes in the environment ensuring that resources are utilized efficiently and QoS requirements are satisfied.

{Model-based Self-Adaptive Resource Allocation in Virtualized Environments}. Huber, Nikolaus; Brosig, Fabian; Kounev, Samuel; in 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2011) (2011). 90–99. ACM, New York, NY, USA.

Acceptance Rate (Full Paper): 27\% (21/76)

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The adoption of virtualization and Cloud Computing technologies promises a number of benefits such as increased flexibility, better energy efficiency and lower operating costs for IT systems. However, highly variable workloads make it challenging to provide quality-of-service guarantees while at the same time ensuring efficient resource utilization. To avoid violations of service-level agreements (SLAs) or inefficient resource usage, resource allocations have to be adapted continuously during operation to reflect changes in application workloads. In this paper, we present a novel approach to self-adaptive resource allocation in virtualized environments based on online architecture-level performance models. We present a detailed case study of a representative enterprise application, the new SPECjEnterprise2010 benchmark, deployed in a virtualized cluster environment. The case study serves as a proof-of-concept demonstrating the effectiveness and practical applicability of our approach.

@inproceedings{HuBrKo2011-SEAMS-ResAlloc,
  abstract = {The adoption of virtualization and Cloud Computing technologies promises a number of benefits such as increased flexibility, better energy efficiency and lower operating costs for IT systems. However, highly variable workloads make it challenging to provide quality-of-service guarantees while at the same time ensuring efficient resource utilization. To avoid violations of service-level agreements (SLAs) or inefficient resource usage, resource allocations have to be adapted continuously during operation to reflect changes in application workloads. In this paper, we present a novel approach to self-adaptive resource allocation in virtualized environments based on online architecture-level performance models. We present a detailed case study of a representative enterprise application, the new SPECjEnterprise2010 benchmark, deployed in a virtualized cluster environment. The case study serves as a proof-of-concept demonstrating the effectiveness and practical applicability of our approach.},
  address = {New York, NY, USA},
  author = {Huber, Nikolaus and Brosig, Fabian and Kounev, Samuel},
  booktitle = {6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2011)},
  keywords = {Self-adaptive-systems},
  month = {05},
  note = {Acceptance Rate (Full Paper): 27\% (21/76)},
  pages = {90–99},
  publisher = {ACM},
  title = {{Model-based Self-Adaptive Resource Allocation in Virtualized Environments}},
  year = 2011
}

%0 Conference Paper
%1 HuBrKo2011-SEAMS-ResAlloc
%A Huber, Nikolaus
%A Brosig, Fabian
%A Kounev, Samuel
%B 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2011)
%C New York, NY, USA
%D 2011
%I ACM
%P 90--99
%T {Model-based Self-Adaptive Resource Allocation in Virtualized Environments}
%U http://dl.acm.org/authorize?425581
%X The adoption of virtualization and Cloud Computing technologies promises a number of benefits such as increased flexibility, better energy efficiency and lower operating costs for IT systems. However, highly variable workloads make it challenging to provide quality-of-service guarantees while at the same time ensuring efficient resource utilization. To avoid violations of service-level agreements (SLAs) or inefficient resource usage, resource allocations have to be adapted continuously during operation to reflect changes in application workloads. In this paper, we present a novel approach to self-adaptive resource allocation in virtualized environments based on online architecture-level performance models. We present a detailed case study of a representative enterprise application, the new SPECjEnterprise2010 benchmark, deployed in a virtualized cluster environment. The case study serves as a proof-of-concept demonstrating the effectiveness and practical applicability of our approach.

Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics. Kounev, Samuel; Bender, Konstantin; Brosig, Fabian; Huber, Nikolaus; Okamoto, Russell; in 4th International ICST Conference on Simulation Tools and Techniques (SIMUtools 2011), March 21--25, 2011, Barcelona, Spain (2011).

Acceptance Rate (Full Paper): 29.8\% (23/77), ICST Best Paper Award

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@inproceedings{KoBeBrHuOk2011-SIMUTools-DataFabrics,
  author = {Kounev, Samuel and Bender, Konstantin and Brosig, Fabian and Huber, Nikolaus and Okamoto, Russell},
  booktitle = {4th International ICST Conference on Simulation Tools and Techniques (SIMUtools 2011), March 21–25, 2011, Barcelona, Spain},
  keywords = {QPME_Bibliography},
  month = {03},
  note = {Acceptance Rate (Full Paper): 29.8\% (23/77), <b>ICST Best Paper Award</b>},
  title = {Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics},
  year = 2011
}

%0 Conference Paper
%1 KoBeBrHuOk2011-SIMUTools-DataFabrics
%A Kounev, Samuel
%A Bender, Konstantin
%A Brosig, Fabian
%A Huber, Nikolaus
%A Okamoto, Russell
%B 4th International ICST Conference on Simulation Tools and Techniques (SIMUtools 2011), March 21--25, 2011, Barcelona, Spain
%D 2011
%T Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics

Online Performance Prediction with Architecture-Level Performance Models. Brosig, Fabian; in Software Engineering (Workshops) - Doctoral Symposium, February 21--25, 2011, R. Reussner, A. Pretschner, S. J{ä}hnichen (eds.) (2011). (Vol. 184) 279–284. GI, Bonn, Germany.

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{Today's enterprise systems based on increasingly complex software architectures often exhibit poor performance and resource efficiency thus having high operating costs. This is due to the inability to predict at run-time the effect of changes in the system environment and adapt the system accordingly. We propose a new performance modeling approach that allows the prediction of performance and system resource utilization online during system operation. We use architecture-level performance models that capture the performance-relevant information of the software architecture, deployment, execution environment and workload. The models will be automatically maintained during operation. To derive performance predictions, we propose a tailorable model solving approach to provide flexibility in view of prediction accuracy and analysis overhead.}

@inproceedings{Br2011-SEDoktoranden-Symposium,
  abstract = {{Today's enterprise systems based on increasingly complex software architectures often exhibit poor performance and resource efficiency thus having high operating costs. This is due to the inability to predict at run-time the effect of changes in the system environment and adapt the system accordingly. We propose a new performance modeling approach that allows the prediction of performance and system resource utilization online during system operation. We use architecture-level performance models that capture the performance-relevant information of the software architecture, deployment, execution environment and workload. The models will be automatically maintained during operation. To derive performance predictions, we propose a tailorable model solving approach to provide flexibility in view of prediction accuracy and analysis overhead.}},
  address = {Bonn, Germany},
  author = {Brosig, Fabian},
  booktitle = {Software Engineering (Workshops) - Doctoral Symposium, February 21–25, 2011},
  editor = {Reussner, Ralf and Pretschner, Alexander and J{\"a}hnichen, Stefan},
  keywords = {t_workshop},
  month = {02},
  pages = {279–284},
  publisher = {GI},
  series = {Lecture Notes in Informatics (LNI)},
  title = {Online Performance Prediction with Architecture-Level Performance Models},
  volume = 184,
  year = 2011
}

%0 Conference Paper
%1 Br2011-SEDoktoranden-Symposium
%A Brosig, Fabian
%B Software Engineering (Workshops) - Doctoral Symposium, February 21--25, 2011
%C Bonn, Germany
%D 2011
%E Reussner, Ralf
%E Pretschner, Alexander
%E J{ä}hnichen, Stefan
%I GI
%P 279--284
%T Online Performance Prediction with Architecture-Level Performance Models
%V 184
%X {Today's enterprise systems based on increasingly complex software architectures often exhibit poor performance and resource efficiency thus having high operating costs. This is due to the inability to predict at run-time the effect of changes in the system environment and adapt the system accordingly. We propose a new performance modeling approach that allows the prediction of performance and system resource utilization online during system operation. We use architecture-level performance models that capture the performance-relevant information of the software architecture, deployment, execution environment and workload. The models will be automatically maintained during operation. To derive performance predictions, we propose a tailorable model solving approach to provide flexibility in view of prediction accuracy and analysis overhead.}

{Analysis of the Performance-Influencing Factors of Virtualization Platforms}. Huber, Nikolaus; von Quast, Marcel; Brosig, Fabian; Kounev, Samuel; in The 12th International Symposium on Distributed Objects, Middleware, and Applications (DOA 2010) (2010). Springer Verlag, Crete, Greece.

Acceptance Rate (Full Paper): 33\%

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Nowadays, virtualization solutions are gaining increasing importance. By enabling the sharing of physical resources, thus making resource usage more efficient, they promise energy and cost savings. Additionally, virtualization is the key enabling technology for Cloud Computing and server consolidation. However, the effects of sharing resources on system performance are not yet well-understood. This makes performance prediction and performance management of services deployed in such dynamic systems very challenging. Because of the large variety of virtualization solutions, a generic approach to predict the performance influences of virtualization platforms is highly desirable. In this paper, we present a hierarchical model capturing the major performance-relevant factors of virtualization platforms. We then propose a general methodology to quantify the influence of the identified factors based on an empirical approach using benchmarks. Finally, we present a case study of Citrix XenServer 5.5, a state-of-the-art virtualization platform.

@inproceedings{HuQuBrKo2010-DOA-AnalysisVirt,
  abstract = {Nowadays, virtualization solutions are gaining increasing importance. By enabling the sharing of physical resources, thus making resource usage more efficient, they promise energy and cost savings. Additionally, virtualization is the key enabling technology for Cloud Computing and server consolidation. However, the effects of sharing resources on system performance are not yet well-understood. This makes performance prediction and performance management of services deployed in such dynamic systems very challenging. Because of the large variety of virtualization solutions, a generic approach to predict the performance influences of virtualization platforms is highly desirable. In this paper, we present a hierarchical model capturing the major performance-relevant factors of virtualization platforms. We then propose a general methodology to quantify the influence of the identified factors based on an empirical approach using benchmarks. Finally, we present a case study of Citrix XenServer 5.5, a state-of-the-art virtualization platform.},
  address = {Crete, Greece},
  author = {Huber, Nikolaus and von Quast, Marcel and Brosig, Fabian and Kounev, Samuel},
  booktitle = {The 12th International Symposium on Distributed Objects, Middleware, and Applications (DOA 2010)},
  keywords = {Virtualization},
  month = 10,
  note = {Acceptance Rate (Full Paper): 33\%},
  publisher = {Springer Verlag},
  title = {{Analysis of the Performance-Influencing Factors of Virtualization Platforms}},
  year = 2010
}

%0 Conference Paper
%1 HuQuBrKo2010-DOA-AnalysisVirt
%A Huber, Nikolaus
%A von Quast, Marcel
%A Brosig, Fabian
%A Kounev, Samuel
%B The 12th International Symposium on Distributed Objects, Middleware, and Applications (DOA 2010)
%C Crete, Greece
%D 2010
%I Springer Verlag
%T {Analysis of the Performance-Influencing Factors of Virtualization Platforms}
%X Nowadays, virtualization solutions are gaining increasing importance. By enabling the sharing of physical resources, thus making resource usage more efficient, they promise energy and cost savings. Additionally, virtualization is the key enabling technology for Cloud Computing and server consolidation. However, the effects of sharing resources on system performance are not yet well-understood. This makes performance prediction and performance management of services deployed in such dynamic systems very challenging. Because of the large variety of virtualization solutions, a generic approach to predict the performance influences of virtualization platforms is highly desirable. In this paper, we present a hierarchical model capturing the major performance-relevant factors of virtualization platforms. We then propose a general methodology to quantify the influence of the identified factors based on an empirical approach using benchmarks. Finally, we present a case study of Citrix XenServer 5.5, a state-of-the-art virtualization platform.

{Towards self-aware performance and resource management in modern service-oriented systems}. Kounev, Samuel; Brosig, Fabian; Huber, Nikolaus; Reussner, Ralf; in Proceedings of the 7th IEEE International Conference on Services Computing (SCC 2010), July 5-10, Miami, Florida, USA (2010). IEEE Computer Society.

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Modern service-oriented systems have increasingly complex loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment (e.g., varying service workloads) and adapt the system configuration accordingly. In this paper, we describe a long-term vision and approach for designing systems with built-in self-aware performance and resource management capabilities. We advocate the use of architecture-level performance models extracted dynamically from the evolving system configuration and maintained automatically during operation. The models will be exploited at run-time to adapt the system to changes in the environment ensuring that resources are utilized efficiently and performance requirements are continuously satisfied.

@inproceedings{KoBrHuRe2010-SCC-Towards,
  abstract = {Modern service-oriented systems have increasingly complex loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment (e.g., varying service workloads) and adapt the system configuration accordingly. In this paper, we describe a long-term vision and approach for designing systems with built-in self-aware performance and resource management capabilities. We advocate the use of architecture-level performance models extracted dynamically from the evolving system configuration and maintained automatically during operation. The models will be exploited at run-time to adapt the system to changes in the environment ensuring that resources are utilized efficiently and performance requirements are continuously satisfied.},
  author = {Kounev, Samuel and Brosig, Fabian and Huber, Nikolaus and Reussner, Ralf},
  booktitle = {Proceedings of the 7th IEEE International Conference on Services Computing (SCC 2010), July 5-10, Miami, Florida, USA},
  keywords = {Self-adaptive-systems},
  month = {07},
  publisher = {IEEE Computer Society},
  title = {{Towards self-aware performance and resource management in modern service-oriented systems}},
  year = 2010
}

%0 Conference Paper
%1 KoBrHuRe2010-SCC-Towards
%A Kounev, Samuel
%A Brosig, Fabian
%A Huber, Nikolaus
%A Reussner, Ralf
%B Proceedings of the 7th IEEE International Conference on Services Computing (SCC 2010), July 5-10, Miami, Florida, USA
%D 2010
%I IEEE Computer Society
%T {Towards self-aware performance and resource management in modern service-oriented systems}
%X Modern service-oriented systems have increasingly complex loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment (e.g., varying service workloads) and adapt the system configuration accordingly. In this paper, we describe a long-term vision and approach for designing systems with built-in self-aware performance and resource management capabilities. We advocate the use of architecture-level performance models extracted dynamically from the evolving system configuration and maintained automatically during operation. The models will be exploited at run-time to adapt the system to changes in the environment ensuring that resources are utilized efficiently and performance requirements are continuously satisfied.

{Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}. Brosig, Fabian; Kounev, Samuel; Krogmann, Klaus; in Proceedings of the 1st International Workshop on Run-time mOdels for Self-managing Systems and Applications (ROSSA 2009). In conjunction with Fourth International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2009), Pisa, Italy, October 19, 2009. (2009). ACM, New York, NY, USA.

[ Abstract ]
[ BibTeX ]
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Nowadays, software systems have to fulfill increasingly stringent requirements for performance and scalability. To ensure that a system meets its performance requirements during operation, the ability to predict its performance under different configurations and workloads is essential. Most performance analysis tools currently used in industry focus on monitoring the current system state. They provide low-level monitoring data without any performance prediction capabilities. For performance prediction, performance models are normally required. However, building predictive performance models manually requires a lot of time and effort. In this paper, we present a method for automated extraction of performance models of Java EE applications, based on monitoring data collected during operation. We extract instances of the Palladio Component Model (PCM) - a performance meta-model targeted at component-based systems. We evaluate the model extraction method in the context of a case study with a real-world enterprise application. Even though the extraction requires some manual intervention, the case study demonstrates that the existing gap between low-level monitoring data and high-level performance models can be closed.

@inproceedings{BrKoKr2009-ROSSA-Extracting_PCM_JavaEE,
  abstract = {Nowadays, software systems have to fulfill increasingly stringent requirements for performance and scalability. To ensure that a system meets its performance requirements during operation, the ability to predict its performance under different configurations and workloads is essential. Most performance analysis tools currently used in industry focus on monitoring the current system state. They provide low-level monitoring data without any performance prediction capabilities. For performance prediction, performance models are normally required. However, building predictive performance models manually requires a lot of time and effort. In this paper, we present a method for automated extraction of performance models of Java EE applications, based on monitoring data collected during operation. We extract instances of the Palladio Component Model (PCM) - a performance meta-model targeted at component-based systems. We evaluate the model extraction method in the context of a case study with a real-world enterprise application. Even though the extraction requires some manual intervention, the case study demonstrates that the existing gap between low-level monitoring data and high-level performance models can be closed.},
  author = {Brosig, Fabian and Kounev, Samuel and Krogmann, Klaus},
  booktitle = {Proceedings of the 1st International Workshop on Run-time mOdels for Self-managing Systems and Applications (ROSSA 2009). In conjunction with Fourth International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2009), Pisa, Italy, October 19, 2009.},
  keywords = {t_workshop},
  month = 10,
  publisher = {ACM, New York, NY, USA},
  title = {{Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}},
  year = 2009
}

%0 Conference Paper
%1 BrKoKr2009-ROSSA-Extracting_PCM_JavaEE
%A Brosig, Fabian
%A Kounev, Samuel
%A Krogmann, Klaus
%B Proceedings of the 1st International Workshop on Run-time mOdels for Self-managing Systems and Applications (ROSSA 2009). In conjunction with Fourth International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2009), Pisa, Italy, October 19, 2009.
%D 2009
%I ACM, New York, NY, USA
%T {Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}
%X Nowadays, software systems have to fulfill increasingly stringent requirements for performance and scalability. To ensure that a system meets its performance requirements during operation, the ability to predict its performance under different configurations and workloads is essential. Most performance analysis tools currently used in industry focus on monitoring the current system state. They provide low-level monitoring data without any performance prediction capabilities. For performance prediction, performance models are normally required. However, building predictive performance models manually requires a lot of time and effort. In this paper, we present a method for automated extraction of performance models of Java EE applications, based on monitoring data collected during operation. We extract instances of the Palladio Component Model (PCM) - a performance meta-model targeted at component-based systems. We evaluate the model extraction method in the context of a case study with a real-world enterprise application. Even though the extraction requires some manual intervention, the case study demonstrates that the existing gap between low-level monitoring data and high-level performance models can be closed.

{Using WebLogic Diagnostics Framework to Enable Performance Prediction for Java EE Applications}. Brosig, Fabian; Kounev, Samuel; Paclat, Charles; (2009, September).

[ Abstract ]
[ BibTeX ]
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Throughout the system life cycle, the ability to predict a software system's performance under different configurations and workloads is highly valuable to ensure that the system meets its performance requirements. During the design phase, performance prediction helps to evaluate different design alternatives. At deployment time, it facilitates system sizing and capacity planning. During operation, predicting the effect of changes in the workload or in the system configuration is beneficial for run-time performance management. The alternative to performance prediction is to deploy the system in an environment reflecting the configuration of interest and conduct experiments measuring the system performance under the respective workloads. Such experiments, however, are normally very expensive and time-consuming and therefore often considered not to be economically viable. To enable performance prediction we need an abstraction of the real system that incorporates performance-relevant data, i.e., a performance model. Based on such a model, performance analysis can be carried out. Unfortunately, building predictive performance models manually requires a lot of time and effort. The model must be designed to reflect the abstract system structure and capture its performance-relevant aspects. In addition, model parameters like resource demands or system configuration parameters have to be determined. Given the costs of building performance models, techniques for automatic extraction of models based on observation of the system at run-time are highly desirable. During system development, such models can be exploited to evaluate the performance of system prototypes. During operation, an automatically extracted performance model can be applied for efficient and performance-aware resource management. For example, if one observes an increased user workload and assumes a steady workload growth rate, performance predictions help to determine when the system would reach its saturation point. This way, system operators can react to the changing workload before the system has failed to meet its performance objectives thus avoiding a violation of service level agreements (SLAs). Current performance analysis tools used in industry mostly focus on profiling and monitoring transaction response times and resource consumption. The tools often provide large amounts of low level data while important information needed for building performance models is missing, e.g., the resource demands of individual components. In this article, we present a method for automated extraction of performance models for Java EE applications during operation. We implemented the method in a tool prototype and evaluated its effectiveness in the context of a case study with an early prototype of the SPECjEnterprise2009 benchmark application which in the following we will refer to as SPECjEnterprise2009_pre. (SPECjEnterprise2009 is the successor benchmark of the SPECjAppServer2004 benchmark developed by the Standard Performance Evaluation Corp. [SPEC]; SPECjEnterprise is a trademark of SPEC. The SPECjEnterprise2009 results or findings in this publication have not been reviewed or accepted by SPEC, therefore no comparison nor performance inference can be made against any published SPEC result.) The target Java EE platform we consider is Oracle WebLogic Server (WLS). The extraction is based on monitoring data that is collected during operation using the WebLogic Diagnostics Framework (WLDF). As a performance model, we selected the Palladio Component Model (PCM). PCM is a sophisticated performance modeling framework with mature tool support. In contrast to low level mathematical models like, e.g., queueing networks, PCM is a high-level UML-like design-oriented model that captures the performance-relevant aspects of the system architecture. This makes PCM models easy to understand and use by software developers. We begin by providing some background on the technologies we use, focusing on the WLDF monitoring framework and the PCM models. We then describe the model extraction method in more detail. Finally, we present the case study we conducted and conclude with a summary.

@misc{BrKoPa2009-OTN-WLDF2PCM,
  abstract = {Throughout the system life cycle, the ability to predict a software system's performance under different configurations and workloads is highly valuable to ensure that the system meets its performance requirements. During the design phase, performance prediction helps to evaluate different design alternatives. At deployment time, it facilitates system sizing and capacity planning. During operation, predicting the effect of changes in the workload or in the system configuration is beneficial for run-time performance management. The alternative to performance prediction is to deploy the system in an environment reflecting the configuration of interest and conduct experiments measuring the system performance under the respective workloads. Such experiments, however, are normally very expensive and time-consuming and therefore often considered not to be economically viable. To enable performance prediction we need an abstraction of the real system that incorporates performance-relevant data, i.e., a performance model. Based on such a model, performance analysis can be carried out. Unfortunately, building predictive performance models manually requires a lot of time and effort. The model must be designed to reflect the abstract system structure and capture its performance-relevant aspects. In addition, model parameters like resource demands or system configuration parameters have to be determined. Given the costs of building performance models, techniques for automatic extraction of models based on observation of the system at run-time are highly desirable. During system development, such models can be exploited to evaluate the performance of system prototypes. During operation, an automatically extracted performance model can be applied for efficient and performance-aware resource management. For example, if one observes an increased user workload and assumes a steady workload growth rate, performance predictions help to determine when the system would reach its saturation point. This way, system operators can react to the changing workload before the system has failed to meet its performance objectives thus avoiding a violation of service level agreements (SLAs). Current performance analysis tools used in industry mostly focus on profiling and monitoring transaction response times and resource consumption. The tools often provide large amounts of low level data while important information needed for building performance models is missing, e.g., the resource demands of individual components. In this article, we present a method for automated extraction of performance models for Java EE applications during operation. We implemented the method in a tool prototype and evaluated its effectiveness in the context of a case study with an early prototype of the SPECjEnterprise2009 benchmark application which in the following we will refer to as SPECjEnterprise2009_pre. (SPECjEnterprise2009 is the successor benchmark of the SPECjAppServer2004 benchmark developed by the Standard Performance Evaluation Corp. [SPEC]; SPECjEnterprise is a trademark of SPEC. The SPECjEnterprise2009 results or findings in this publication have not been reviewed or accepted by SPEC, therefore no comparison nor performance inference can be made against any published SPEC result.) The target Java EE platform we consider is Oracle WebLogic Server (WLS). The extraction is based on monitoring data that is collected during operation using the WebLogic Diagnostics Framework (WLDF). As a performance model, we selected the Palladio Component Model (PCM). PCM is a sophisticated performance modeling framework with mature tool support. In contrast to low level mathematical models like, e.g., queueing networks, PCM is a high-level UML-like design-oriented model that captures the performance-relevant aspects of the system architecture. This makes PCM models easy to understand and use by software developers. We begin by providing some background on the technologies we use, focusing on the WLDF monitoring framework and the PCM models. We then describe the model extraction method in more detail. Finally, we present the case study we conducted and conclude with a summary.},
  author = {Brosig, Fabian and Kounev, Samuel and Paclat, Charles},
  howpublished = {Oracle Technology Network (OTN) Article},
  keywords = {Instrumentation_profiling_and_workload_characterization},
  month = {09},
  title = {{Using WebLogic Diagnostics Framework to Enable Performance Prediction for Java EE Applications}},
  year = 2009
}

%0 Generic
%1 BrKoPa2009-OTN-WLDF2PCM
%A Brosig, Fabian
%A Kounev, Samuel
%A Paclat, Charles
%D 2009
%T {Using WebLogic Diagnostics Framework to Enable Performance Prediction for Java EE Applications}
%U http://www.oracle.com/technetwork/articles/brosig-wldf-086367.html
%X Throughout the system life cycle, the ability to predict a software system's performance under different configurations and workloads is highly valuable to ensure that the system meets its performance requirements. During the design phase, performance prediction helps to evaluate different design alternatives. At deployment time, it facilitates system sizing and capacity planning. During operation, predicting the effect of changes in the workload or in the system configuration is beneficial for run-time performance management. The alternative to performance prediction is to deploy the system in an environment reflecting the configuration of interest and conduct experiments measuring the system performance under the respective workloads. Such experiments, however, are normally very expensive and time-consuming and therefore often considered not to be economically viable. To enable performance prediction we need an abstraction of the real system that incorporates performance-relevant data, i.e., a performance model. Based on such a model, performance analysis can be carried out. Unfortunately, building predictive performance models manually requires a lot of time and effort. The model must be designed to reflect the abstract system structure and capture its performance-relevant aspects. In addition, model parameters like resource demands or system configuration parameters have to be determined. Given the costs of building performance models, techniques for automatic extraction of models based on observation of the system at run-time are highly desirable. During system development, such models can be exploited to evaluate the performance of system prototypes. During operation, an automatically extracted performance model can be applied for efficient and performance-aware resource management. For example, if one observes an increased user workload and assumes a steady workload growth rate, performance predictions help to determine when the system would reach its saturation point. This way, system operators can react to the changing workload before the system has failed to meet its performance objectives thus avoiding a violation of service level agreements (SLAs). Current performance analysis tools used in industry mostly focus on profiling and monitoring transaction response times and resource consumption. The tools often provide large amounts of low level data while important information needed for building performance models is missing, e.g., the resource demands of individual components. In this article, we present a method for automated extraction of performance models for Java EE applications during operation. We implemented the method in a tool prototype and evaluated its effectiveness in the context of a case study with an early prototype of the SPECjEnterprise2009 benchmark application which in the following we will refer to as SPECjEnterprise2009_pre. (SPECjEnterprise2009 is the successor benchmark of the SPECjAppServer2004 benchmark developed by the Standard Performance Evaluation Corp. [SPEC]; SPECjEnterprise is a trademark of SPEC. The SPECjEnterprise2009 results or findings in this publication have not been reviewed or accepted by SPEC, therefore no comparison nor performance inference can be made against any published SPEC result.) The target Java EE platform we consider is Oracle WebLogic Server (WLS). The extraction is based on monitoring data that is collected during operation using the WebLogic Diagnostics Framework (WLDF). As a performance model, we selected the Palladio Component Model (PCM). PCM is a sophisticated performance modeling framework with mature tool support. In contrast to low level mathematical models like, e.g., queueing networks, PCM is a high-level UML-like design-oriented model that captures the performance-relevant aspects of the system architecture. This makes PCM models easy to understand and use by software developers. We begin by providing some background on the technologies we use, focusing on the WLDF monitoring framework and the PCM models. We then describe the model extraction method in more detail. Finally, we present the case study we conducted and conclude with a summary.

{Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}. Brosig, Fabian; (2009, June). Universit{{ä}}t Karlsruhe (TH), Karlsruhe, Germany.

FZI Prize "Best Diploma Thesis"

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@mastersthesis{brosig2009a,
  address = {Karlsruhe, Germany},
  author = {Brosig, Fabian},
  keywords = {Instrumentation_profiling_and_workload_characterization},
  month = {06},
  note = {<b>FZI Prize "Best Diploma Thesis"</b>},
  school = {Universit{{\"a}}t Karlsruhe (TH)},
  title = {{Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}},
  year = 2009
}

%0 Thesis
%1 brosig2009a
%A Brosig, Fabian
%C Karlsruhe, Germany
%D 2009
%T {Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}

{S}oftware {Z}ertifizierung. (4/2008), Blomer, Jakob; Brosig, Fabian; Kreidler, Andreas; K{"u}ttel, Jens; Kuwertz, Achim; Liebel, Grischa; Popovic, Daniel; St{"u}bs, Michael; Turek, Alexander M.; Vogel, Christian; Weinstein, Thomas; Wurth, Thomas; (T. Goldschmidt; H. Groenda; K. Krogmann; M. Kuperberg; A. Martens; C. Rathfelder; R. Reussner; J. Stammel, eds.) (2008). Universit{ä}t Karlsruhe, Fakult{ä}t f{ü}r Informatik.

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Systematische Qualit{ä}tssicherung gewinnt im Rahmen des globalenWettbewerbs auch in der Software-Entwicklungsbranche zunehmend an Bedeutung. Vor allem auf dem Weg zur Software-Industrialisierung bzw. zu einer ingenieurm{ä}ss{}igen Software-Entwicklung ist eine durchg{ä}ngige Qualit{ä}tssicherung unabdingbar. Zertifizierungen bieten hierbei die M{ö}glichkeit, die Einhaltung bestimmter Standards und Kriterien durch unabh{ä}ngige Dritte {ü}berpr{ü}fen und bescheinigen zu lassen, um die Qualit{ä}t eines Produktes oder Entwicklungsprozesses zu belegen. Zertifizierungen k{ö}nnen sich sowohl auf Produkte und Prozesse als auch auf die Ausbildung und das Wissen von Einzelpersonen beziehen. Da Zertifikate durch unabh{ä}ngige Pr{ü}finstanzen ausgestellt werden, wird Zertifikaten und deren {ü}berpr{ü}fbaren Aussagen im Allgemeinen ein deutlich h{ö}heres Vertrauen entgegengebracht als Qualit{ä}tsversprechen von Software-Herstellern selbst. Unternehmen, die ihre Prozesse beispielsweise nach CMMI zertifizieren lassen, k{ö}nnen damit ihre F{ä}higkeiten unter Beweis stellen, Projekte erfolgreich und mit vorhersagbarer Qualit{ä}t abschlie{ss}en zu k{ö}nnen. Neben dem Nachweis entsprechender Zertifikate als Diversifikationsmerkmal gegen{ü}ber Mitbewerbern k{ö}nnen Zertifikate {ü}ber die Einhaltung von Standards auch durch den Gesetzgeber vorgeschrieben werden. Ein Beispiel hierf{ü}r sind Zertifikate aus Hochsicherheitsbereichen wie Atomkraftwerken. Das Seminar wurde wie eine wissenschaftliche Konferenz organisiert: Die Einreichungen wurden in einem zweistufigen Peer-Review-Verfahren begutachtet. In der ersten Stufe wurde eine Begutachtung der studentischen Arbeiten durch Kommilitonen durchgef{ü}hrt, in der zweiten Stufe eine Begutachtung durch die Betreuer. In verschiedenen Sessions wurden die Artikel an zwei Konferenztagen pr{ä}sentiert. Die besten Beitr{ä}ge wurden durch best paper awards ausgezeichnet. Diese gingen an Fabian Brosig f{ü}r seine Arbeit Cost Benefit Analysis Method (CBAM), an Jakob Blomer f{ü}r die Arbeit Zertifizierung von Softwarebenchmarks und an Grischa Liebel f{ü}r die Arbeit SWT - Das Standard Widget Toolkit, denen hiermit noch einmal herzlich zu dieser herausragenden Leistung gratuliert wird. Erg{ä}nzend zu den Vortr{ä}gen der Seminarteilnehmer wurde ein eingeladener Vortrag gehalten. Herr Dr. Dirk Feuerhelm von der 1&1 Internet AG gab dabei dankenswerterweise in seinem Vortrag mit dem Thema Softskills -- Ist das objektorientiert oder modellgetrieben? einen Einblick in die Aufgaben als Leiter der Software-Entwicklung

@techreport{groenda2008c,
  abstract = {Systematische Qualit{\"a}tssicherung gewinnt im Rahmen des globalenWettbewerbs auch in der Software-Entwicklungsbranche zunehmend an Bedeutung. Vor allem auf dem Weg zur Software-Industrialisierung bzw. zu einer ingenieurm{\"a}ss{}igen Software-Entwicklung ist eine durchg{\"a}ngige Qualit{\"a}tssicherung unabdingbar. Zertifizierungen bieten hierbei die M{\"o}glichkeit, die Einhaltung bestimmter Standards und Kriterien durch unabh{\"a}ngige Dritte {\"u}berpr{\"u}fen und bescheinigen zu lassen, um die Qualit{\"a}t eines Produktes oder Entwicklungsprozesses zu belegen. Zertifizierungen k{\"o}nnen sich sowohl auf Produkte und Prozesse als auch auf die Ausbildung und das Wissen von Einzelpersonen beziehen. Da Zertifikate durch unabh{\"a}ngige Pr{\"u}finstanzen ausgestellt werden, wird Zertifikaten und deren {\"u}berpr{\"u}fbaren Aussagen im Allgemeinen ein deutlich h{\"o}heres Vertrauen entgegengebracht als Qualit{\"a}tsversprechen von Software-Herstellern selbst. Unternehmen, die ihre Prozesse beispielsweise nach CMMI zertifizieren lassen, k{\"o}nnen damit ihre F{\"a}higkeiten unter Beweis stellen, Projekte erfolgreich und mit vorhersagbarer Qualit{\"a}t abschlie{ss}en zu k{\"o}nnen. Neben dem Nachweis entsprechender Zertifikate als Diversifikationsmerkmal gegen{\"u}ber Mitbewerbern k{\"o}nnen Zertifikate {\"u}ber die Einhaltung von Standards auch durch den Gesetzgeber vorgeschrieben werden. Ein Beispiel hierf{\"u}r sind Zertifikate aus Hochsicherheitsbereichen wie Atomkraftwerken. Das Seminar wurde wie eine wissenschaftliche Konferenz organisiert: Die Einreichungen wurden in einem zweistufigen Peer-Review-Verfahren begutachtet. In der ersten Stufe wurde eine Begutachtung der studentischen Arbeiten durch Kommilitonen durchgef{\"u}hrt, in der zweiten Stufe eine Begutachtung durch die Betreuer. In verschiedenen Sessions wurden die Artikel an zwei Konferenztagen pr{\"a}sentiert. Die besten Beitr{\"a}ge wurden durch best paper awards ausgezeichnet. Diese gingen an Fabian Brosig f{\"u}r seine Arbeit Cost Benefit Analysis Method (CBAM), an Jakob Blomer f{\"u}r die Arbeit Zertifizierung von Softwarebenchmarks und an Grischa Liebel f{\"u}r die Arbeit SWT - Das Standard Widget Toolkit, denen hiermit noch einmal herzlich zu dieser herausragenden Leistung gratuliert wird. Erg{\"a}nzend zu den Vortr{\"a}gen der Seminarteilnehmer wurde ein eingeladener Vortrag gehalten. Herr Dr. Dirk Feuerhelm von der 1&1 Internet AG gab dabei dankenswerterweise in seinem Vortrag mit dem Thema Softskills – Ist das objektorientiert oder modellgetrieben? einen Einblick in die Aufgaben als Leiter der Software-Entwicklung},
  author = {Blomer, Jakob and Brosig, Fabian and Kreidler, Andreas and K{"u}ttel, Jens and Kuwertz, Achim and Liebel, Grischa and Popovic, Daniel and St{"u}bs, Michael and Turek, Alexander M. and Vogel, Christian and Weinstein, Thomas and Wurth, Thomas},
  editor = {Goldschmidt, Thomas and Groenda, Henning and Krogmann, Klaus and Kuperberg, Michael and Martens, Anne and Rathfelder, Christoph and Reussner, Ralf and Stammel, Johannes},
  institution = {Universit{\"a}t Karlsruhe, Fakult{\"a}t f{\"u}r Informatik},
  keywords = {se2},
  number = {4/2008},
  title = {{S}oftware {Z}ertifizierung},
  year = 2008
}

%0 Report
%1 groenda2008c
%A Blomer, Jakob
%A Brosig, Fabian
%A Kreidler, Andreas
%A K{"u}ttel, Jens
%A Kuwertz, Achim
%A Liebel, Grischa
%A Popovic, Daniel
%A St{"u}bs, Michael
%A Turek, Alexander M.
%A Vogel, Christian
%A Weinstein, Thomas
%A Wurth, Thomas
%D 2008
%E Goldschmidt, Thomas
%E Groenda, Henning
%E Krogmann, Klaus
%E Kuperberg, Michael
%E Martens, Anne
%E Rathfelder, Christoph
%E Reussner, Ralf
%E Stammel, Johannes
%N 4/2008
%T {S}oftware {Z}ertifizierung
%X Systematische Qualit{ä}tssicherung gewinnt im Rahmen des globalenWettbewerbs auch in der Software-Entwicklungsbranche zunehmend an Bedeutung. Vor allem auf dem Weg zur Software-Industrialisierung bzw. zu einer ingenieurm{ä}ss{}igen Software-Entwicklung ist eine durchg{ä}ngige Qualit{ä}tssicherung unabdingbar. Zertifizierungen bieten hierbei die M{ö}glichkeit, die Einhaltung bestimmter Standards und Kriterien durch unabh{ä}ngige Dritte {ü}berpr{ü}fen und bescheinigen zu lassen, um die Qualit{ä}t eines Produktes oder Entwicklungsprozesses zu belegen. Zertifizierungen k{ö}nnen sich sowohl auf Produkte und Prozesse als auch auf die Ausbildung und das Wissen von Einzelpersonen beziehen. Da Zertifikate durch unabh{ä}ngige Pr{ü}finstanzen ausgestellt werden, wird Zertifikaten und deren {ü}berpr{ü}fbaren Aussagen im Allgemeinen ein deutlich h{ö}heres Vertrauen entgegengebracht als Qualit{ä}tsversprechen von Software-Herstellern selbst. Unternehmen, die ihre Prozesse beispielsweise nach CMMI zertifizieren lassen, k{ö}nnen damit ihre F{ä}higkeiten unter Beweis stellen, Projekte erfolgreich und mit vorhersagbarer Qualit{ä}t abschlie{ss}en zu k{ö}nnen. Neben dem Nachweis entsprechender Zertifikate als Diversifikationsmerkmal gegen{ü}ber Mitbewerbern k{ö}nnen Zertifikate {ü}ber die Einhaltung von Standards auch durch den Gesetzgeber vorgeschrieben werden. Ein Beispiel hierf{ü}r sind Zertifikate aus Hochsicherheitsbereichen wie Atomkraftwerken. Das Seminar wurde wie eine wissenschaftliche Konferenz organisiert: Die Einreichungen wurden in einem zweistufigen Peer-Review-Verfahren begutachtet. In der ersten Stufe wurde eine Begutachtung der studentischen Arbeiten durch Kommilitonen durchgef{ü}hrt, in der zweiten Stufe eine Begutachtung durch die Betreuer. In verschiedenen Sessions wurden die Artikel an zwei Konferenztagen pr{ä}sentiert. Die besten Beitr{ä}ge wurden durch best paper awards ausgezeichnet. Diese gingen an Fabian Brosig f{ü}r seine Arbeit Cost Benefit Analysis Method (CBAM), an Jakob Blomer f{ü}r die Arbeit Zertifizierung von Softwarebenchmarks und an Grischa Liebel f{ü}r die Arbeit SWT - Das Standard Widget Toolkit, denen hiermit noch einmal herzlich zu dieser herausragenden Leistung gratuliert wird. Erg{ä}nzend zu den Vortr{ä}gen der Seminarteilnehmer wurde ein eingeladener Vortrag gehalten. Herr Dr. Dirk Feuerhelm von der 1&1 Internet AG gab dabei dankenswerterweise in seinem Vortrag mit dem Thema Softskills -- Ist das objektorientiert oder modellgetrieben? einen Einblick in die Aufgaben als Leiter der Software-Entwicklung

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