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

{Model-based Autonomic and Performance-aware System Adaptation in Heterogeneous Resource Environments: A Case Study}. Huber, Nikolaus; Walter, J{ü}rgen; B{ä}hr, Manuel; Kounev, Samuel; in Proceedings of the 2015 IEEE International Conference on Cloud and Autonomic Computing (ICCAC) (2015). IEEE.

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Recent trends like cloud computing show that service providers increasingly adopt to modern self-adaptive system architectures promising higher resource efficiency and lower operating costs. Blue Yonder is such a service provider in the field of predictive analytics, looking for novel approaches to automatically adapt their service infrastructure to changing customer workloads and service-level agreements. In this paper, we present a realistic case study in which we apply a holistic modelbased approach to build a self-adaptive system that automatically maintains performance requirements and resource efficiency in the heterogeneous resource environment of Blue Yonder. As modeling formalism, we employ the Descartes Modeling Language (DML), a domain-specific language for modeling the performance behavior and run-time adaptation processes of modern dynamic IT systems. This case study is an exemplar for applying a holistic model-based approach to engineering performance-aware system adaptation. More specifically, the results evaluate the applicability and effectiveness of our approach and demonstrate that DML provides suitable modeling abstractions that can be used as a basis for self-adaptive performance and resource management.

@inproceedings{HuWaBaKo2015-ICCAC-BlueYonder,
  abstract = {Recent trends like cloud computing show that service providers increasingly adopt to modern self-adaptive system architectures promising higher resource efficiency and lower operating costs. Blue Yonder is such a service provider in the field of predictive analytics, looking for novel approaches to automatically adapt their service infrastructure to changing customer workloads and service-level agreements. In this paper, we present a realistic case study in which we apply a holistic modelbased approach to build a self-adaptive system that automatically maintains performance requirements and resource efficiency in the heterogeneous resource environment of Blue Yonder. As modeling formalism, we employ the Descartes Modeling Language (DML), a domain-specific language for modeling the performance behavior and run-time adaptation processes of modern dynamic IT systems. This case study is an exemplar for applying a holistic model-based approach to engineering performance-aware system adaptation. More specifically, the results evaluate the applicability and effectiveness of our approach and demonstrate that DML provides suitable modeling abstractions that can be used as a basis for self-adaptive performance and resource management.},
  author = {Huber, Nikolaus and Walter, J{\"u}rgen and B{\"a}hr, Manuel and Kounev, Samuel},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Cloud and Autonomic Computing (ICCAC)},
  keywords = {Self-adaptive-systems},
  month = {09},
  publisher = {IEEE},
  title = {{Model-based Autonomic and Performance-aware System Adaptation in Heterogeneous Resource Environments: A Case Study}},
  year = 2015
}

%0 Conference Paper
%1 HuWaBaKo2015-ICCAC-BlueYonder
%A Huber, Nikolaus
%A Walter, J{ü}rgen
%A B{ä}hr, Manuel
%A Kounev, Samuel
%B Proceedings of the 2015 IEEE International Conference on Cloud and Autonomic Computing (ICCAC)
%D 2015
%I IEEE
%T {Model-based Autonomic and Performance-aware System Adaptation in Heterogeneous Resource Environments: A Case Study}
%X Recent trends like cloud computing show that service providers increasingly adopt to modern self-adaptive system architectures promising higher resource efficiency and lower operating costs. Blue Yonder is such a service provider in the field of predictive analytics, looking for novel approaches to automatically adapt their service infrastructure to changing customer workloads and service-level agreements. In this paper, we present a realistic case study in which we apply a holistic modelbased approach to build a self-adaptive system that automatically maintains performance requirements and resource efficiency in the heterogeneous resource environment of Blue Yonder. As modeling formalism, we employ the Descartes Modeling Language (DML), a domain-specific language for modeling the performance behavior and run-time adaptation processes of modern dynamic IT systems. This case study is an exemplar for applying a holistic model-based approach to engineering performance-aware system adaptation. More specifically, the results evaluate the applicability and effectiveness of our approach and demonstrate that DML provides suitable modeling abstractions that can be used as a basis for self-adaptive performance and resource management.

{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}

{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

Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures. Huber, Nikolaus; (2014, July). Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

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@phdthesis{Huber2014-KIT-AutonomicPerformanceAwareResourceManagement,
  author = {Huber, Nikolaus},
  keywords = {Self-adaptive-systems},
  month = {07},
  school = {Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany},
  title = {Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures},
  year = 2014
}

%0 Thesis
%1 Huber2014-KIT-AutonomicPerformanceAwareResourceManagement
%A Huber, Nikolaus
%D 2014
%T Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures
%U http://nbn-resolving.org/urn:nbn:de:swb:90-432462

{Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}. Herbst, Nikolas Roman; Huber, Nikolaus; Kounev, Samuel; Amrehn, Erich; in Concurrency and Computation - Practice and Experience, John Wiley and Sons, Ltd. (2014). 26(12) 2053–2078.

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As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis covers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.

@article{HuKoAm2013-CCPE-WorkloadClassificationAndForecasting,
  abstract = {As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis covers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.},
  author = {Herbst, Nikolas Roman and Huber, Nikolaus and Kounev, Samuel and Amrehn, Erich},
  journal = {Concurrency and Computation - Practice and Experience, John Wiley and Sons, Ltd.},
  keywords = {Self-adaptive-systems},
  month = {03},
  number = 12,
  pages = {2053–2078},
  title = {{Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}},
  volume = 26,
  year = 2014
}

%0 Journal Article
%1 HuKoAm2013-CCPE-WorkloadClassificationAndForecasting
%A Herbst, Nikolas Roman
%A Huber, Nikolaus
%A Kounev, Samuel
%A Amrehn, Erich
%D 2014
%J Concurrency and Computation - Practice and Experience, John Wiley and Sons, Ltd.
%N 12
%P 2053--2078
%T {Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}
%U http://dx.doi.org/10.1002/cpe.3224
%V 26
%X As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis covers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.

{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}

{Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}. Herbst, Nikolas Roman; Huber, Nikolaus; Kounev, Samuel; Amrehn, Erich; in Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE 2013) (2013). 187–198. ACM, New York, NY, USA.

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{As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis covers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.}

@inproceedings{HeHuKoAm2013-ICPE-WorkloadClassificationAndForecasting,
  abstract = {{As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis covers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.}},
  address = {New York, NY, USA},
  author = {Herbst, Nikolas Roman and Huber, Nikolaus and Kounev, Samuel and Amrehn, Erich},
  booktitle = {Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE 2013)},
  keywords = {Self-adaptive-systems},
  month = {04},
  pages = {187–198},
  publisher = {ACM},
  title = {{Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}},
  year = 2013
}

%0 Conference Paper
%1 HeHuKoAm2013-ICPE-WorkloadClassificationAndForecasting
%A Herbst, Nikolas Roman
%A Huber, Nikolaus
%A Kounev, Samuel
%A Amrehn, Erich
%B Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE 2013)
%C New York, NY, USA
%D 2013
%I ACM
%P 187--198
%T {Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}
%U http://doi.acm.org/10.1145/2479871.2479899
%X {As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis covers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.}

Deriving performance-relevant infrastructure properties through model-based experiments with Ginpex. Hauck, Michael; Kuperberg, Michael; Huber, Nikolaus; Reussner, Ralf; in Software & Systems Modeling (2013). 1–21. Springer-Verlag.

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To predict the performance of an application, it is crucial to consider the performance of the underlying infrastructure. Thus, to yield accurate prediction results, performance-relevant properties and behaviour of the infrastructure have to be integrated into performance models. However, capturing these properties is a cumbersome and error-prone task, as it requires carefully engineered measurements and experiments. Existing approaches for creating infrastructure performance models require manual coding of these experiments, or ignore the detailed properties in the models. The contribution of this paper is the Ginpex approach, which introduces goal-oriented and model-based specification and generation of executable performance experiments for automatically detecting and quantifying performance-relevant infrastructure properties. Ginpex provides a metamodel for experiment specification and comes with predefined experiment templates that provide automated experiment execution on the target platform and also automate the evaluation of the experiment results. We evaluate Ginpex using three case studies, where experiments are executed to quantify various infrastructure properties.

@article{hauck2013a,
  abstract = {To predict the performance of an application, it is crucial to consider the performance of the underlying infrastructure. Thus, to yield accurate prediction results, performance-relevant properties and behaviour of the infrastructure have to be integrated into performance models. However, capturing these properties is a cumbersome and error-prone task, as it requires carefully engineered measurements and experiments. Existing approaches for creating infrastructure performance models require manual coding of these experiments, or ignore the detailed properties in the models. The contribution of this paper is the Ginpex approach, which introduces goal-oriented and model-based specification and generation of executable performance experiments for automatically detecting and quantifying performance-relevant infrastructure properties. Ginpex provides a metamodel for experiment specification and comes with predefined experiment templates that provide automated experiment execution on the target platform and also automate the evaluation of the experiment results. We evaluate Ginpex using three case studies, where experiments are executed to quantify various infrastructure properties.},
  author = {Hauck, Michael and Kuperberg, Michael and Huber, Nikolaus and Reussner, Ralf},
  journal = {Software & Systems Modeling},
  keywords = {Virtualization},
  pages = {1-21},
  publisher = {Springer-Verlag},
  title = {Deriving performance-relevant infrastructure properties through model-based experiments with Ginpex},
  year = 2013
}

%0 Journal Article
%1 hauck2013a
%A Hauck, Michael
%A Kuperberg, Michael
%A Huber, Nikolaus
%A Reussner, Ralf
%D 2013
%I Springer-Verlag
%J Software & Systems Modeling
%P 1-21
%T Deriving performance-relevant infrastructure properties through model-based experiments with Ginpex
%U http://dx.doi.org/10.1007/s10270-013-0335-7
%X To predict the performance of an application, it is crucial to consider the performance of the underlying infrastructure. Thus, to yield accurate prediction results, performance-relevant properties and behaviour of the infrastructure have to be integrated into performance models. However, capturing these properties is a cumbersome and error-prone task, as it requires carefully engineered measurements and experiments. Existing approaches for creating infrastructure performance models require manual coding of these experiments, or ignore the detailed properties in the models. The contribution of this paper is the Ginpex approach, which introduces goal-oriented and model-based specification and generation of executable performance experiments for automatically detecting and quantifying performance-relevant infrastructure properties. Ginpex provides a metamodel for experiment specification and comes with predefined experiment templates that provide automated experiment execution on the target platform and also automate the evaluation of the experiment results. We evaluate Ginpex using three case studies, where experiments are executed to quantify various infrastructure properties.

{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\%

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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

{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

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@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.

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{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.

{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.

{Ginpex: Deriving Performance-relevant Infrastructure Properties Through Goal-oriented Experiments}. Hauck, Michael; Kuperberg, Michael; Huber, Nikolaus; Reussner, Ralf; in Proceedings of the 7th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2011) (2011). 53–62. ACM, New York, NY, USA.

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@inproceedings{hauck2011b,
  address = {New York, NY, USA},
  author = {Hauck, Michael and Kuperberg, Michael and Huber, Nikolaus and Reussner, Ralf},
  booktitle = {Proceedings of the 7th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2011)},
  keywords = {Virtualization},
  month = {06},
  pages = {53–62},
  publisher = {ACM},
  title = {{Ginpex: Deriving Performance-relevant Infrastructure Properties Through Goal-oriented Experiments}},
  year = 2011
}

%0 Conference Paper
%1 hauck2011b
%A Hauck, Michael
%A Kuperberg, Michael
%A Huber, Nikolaus
%A Reussner, Ralf
%B Proceedings of the 7th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2011)
%C New York, NY, USA
%D 2011
%I ACM
%P 53--62
%T {Ginpex: Deriving Performance-relevant Infrastructure Properties Through Goal-oriented Experiments}

{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)

[ Abstract ]
[ BibTeX ]
[ EndNote ]
[ URL ]
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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.

{E}valuating and {M}odeling {V}irtualization {P}erformance {O}verhead for {C}loud {E}nvironments. Huber, Nikolaus; von Quast, Marcel; Hauck, Michael; Kounev, Samuel; in Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER 2011) (2011). 563–573. SciTePress.

Acceptance Rate: 18/164 = 10.9\%, Best Paper Award

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Due to trends like Cloud Computing and Green IT, virtualization technologies are gaining increasing importance. They promise energy and cost savings by sharing physical resources, thus making resource usage more efficient. However, resource sharing and other factors have direct effects on system performance, which are not yet well-understood. Hence, performance prediction and performance management of services deployed in virtualized environments like public and private Clouds is a challenging task. Because of the large variety of virtualization solutions, a generic approach to predict the performance overhead of services running on virtualization platforms is highly desirable. In this paper, we present experimental results on two popular state-of-the-art virtualization platforms, Citrix XenServer 5.5 and VMware ESX 4.0, as representatives of the two major hypervisor architectures. Based on these results, we propose a basic, generic performance prediction model for the two different types of hypervisor architectures. The target is to predict the performance overhead for executing services on virtualized platforms.

@inproceedings{HuQuHaKo2011-CLOSER-ModelVirtOverhead,
  abstract = {Due to trends like Cloud Computing and Green IT, virtualization technologies are gaining increasing importance. They promise energy and cost savings by sharing physical resources, thus making resource usage more efficient. However, resource sharing and other factors have direct effects on system performance, which are not yet well-understood. Hence, performance prediction and performance management of services deployed in virtualized environments like public and private Clouds is a challenging task. Because of the large variety of virtualization solutions, a generic approach to predict the performance overhead of services running on virtualization platforms is highly desirable. In this paper, we present experimental results on two popular state-of-the-art virtualization platforms, Citrix XenServer 5.5 and VMware ESX 4.0, as representatives of the two major hypervisor architectures. Based on these results, we propose a basic, generic performance prediction model for the two different types of hypervisor architectures. The target is to predict the performance overhead for executing services on virtualized platforms.},
  author = {Huber, Nikolaus and von Quast, Marcel and Hauck, Michael and Kounev, Samuel},
  booktitle = {Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER 2011)},
  keywords = {Virtualization},
  month = {05},
  note = {Acceptance Rate: 18/164 = 10.9\%, <b>Best Paper Award</b>},
  pages = {563 – 573},
  publisher = {SciTePress},
  title = {{E}valuating and {M}odeling {V}irtualization {P}erformance {O}verhead for {C}loud {E}nvironments},
  year = 2011
}

%0 Conference Paper
%1 HuQuHaKo2011-CLOSER-ModelVirtOverhead
%A Huber, Nikolaus
%A von Quast, Marcel
%A Hauck, Michael
%A Kounev, Samuel
%B Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER 2011)
%D 2011
%I SciTePress
%P 563 -- 573
%T {E}valuating and {M}odeling {V}irtualization {P}erformance {O}verhead for {C}loud {E}nvironments
%X Due to trends like Cloud Computing and Green IT, virtualization technologies are gaining increasing importance. They promise energy and cost savings by sharing physical resources, thus making resource usage more efficient. However, resource sharing and other factors have direct effects on system performance, which are not yet well-understood. Hence, performance prediction and performance management of services deployed in virtualized environments like public and private Clouds is a challenging task. Because of the large variety of virtualization solutions, a generic approach to predict the performance overhead of services running on virtualization platforms is highly desirable. In this paper, we present experimental results on two popular state-of-the-art virtualization platforms, Citrix XenServer 5.5 and VMware ESX 4.0, as representatives of the two major hypervisor architectures. Based on these results, we propose a basic, generic performance prediction model for the two different types of hypervisor architectures. The target is to predict the performance overhead for executing services on virtualized platforms.

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

{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\%

[ Abstract ]
[ BibTeX ]
[ EndNote ]
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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.

{Performance Modeling in Industry: A Case Study on Storage Virtualization}. Huber, Nikolaus; Becker, Steffen; Rathfelder, Christoph; Schweflinghaus, Jochen; Reussner, Ralf; in ACM/IEEE 32nd International Conference on Software Engineering (ICSE 2010), Software Engineering in Practice Track (2010). 1–10. ACM, New York, NY, USA.

Acceptance Rate (Full Paper): 23\% (16/71)

[ Abstract ]
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In software engineering, performance and the integration of performance analysis methodologies gain increasing importance, especially for complex systems. Well-developed methods and tools can predict non-functional performance properties like response time or resource utilization in early design stages, thus promising time and cost savings. However, as performance modeling and performance prediction is still a young research area, the methods are not yet well-established and in wide-spread industrial use. This work is a case study of the applicability of the Palladio Component Model as a performance prediction method in an industrial environment. We model and analyze different design alternatives for storage virtualization on an IBM (Trademark of IBM in USA and/or other countries) system. The model calibration, validation and evaluation is based on data measured on a System z9 (Trademark of IBM in USA and/or other countries) as a proof of concept. The results show that performance predictions can identify performance bottlenecks and evaluate design alternatives in early stages of system development. The experiences gained were that performance modeling helps to understand and analyze a system. Hence, this case study substantiates that performance modeling is applicable in industry and a valuable method for evaluating design decisions.

@inproceedings{HuBeRaScRe2010-ICSE-PerfMod,
  abstract = {In software engineering, performance and the integration of performance analysis methodologies gain increasing importance, especially for complex systems. Well-developed methods and tools can predict non-functional performance properties like response time or resource utilization in early design stages, thus promising time and cost savings. However, as performance modeling and performance prediction is still a young research area, the methods are not yet well-established and in wide-spread industrial use. This work is a case study of the applicability of the Palladio Component Model as a performance prediction method in an industrial environment. We model and analyze different design alternatives for storage virtualization on an IBM (Trademark of IBM in USA and/or other countries) system. The model calibration, validation and evaluation is based on data measured on a System z9 (Trademark of IBM in USA and/or other countries) as a proof of concept. The results show that performance predictions can identify performance bottlenecks and evaluate design alternatives in early stages of system development. The experiences gained were that performance modeling helps to understand and analyze a system. Hence, this case study substantiates that performance modeling is applicable in industry and a valuable method for evaluating design decisions.},
  address = {New York, NY, USA},
  author = {Huber, Nikolaus and Becker, Steffen and Rathfelder, Christoph and Schweflinghaus, Jochen and Reussner, Ralf},
  booktitle = {ACM/IEEE 32nd International Conference on Software Engineering (ICSE 2010), Software Engineering in Practice Track},
  keywords = {Automated_model_learning},
  month = {05},
  note = {Acceptance Rate (Full Paper): 23\% (16/71)},
  pages = {1–10},
  publisher = {ACM},
  title = {{Performance Modeling in Industry: A Case Study on Storage Virtualization}},
  year = 2010
}

%0 Conference Paper
%1 HuBeRaScRe2010-ICSE-PerfMod
%A Huber, Nikolaus
%A Becker, Steffen
%A Rathfelder, Christoph
%A Schweflinghaus, Jochen
%A Reussner, Ralf
%B ACM/IEEE 32nd International Conference on Software Engineering (ICSE 2010), Software Engineering in Practice Track
%C New York, NY, USA
%D 2010
%I ACM
%P 1--10
%T {Performance Modeling in Industry: A Case Study on Storage Virtualization}
%X In software engineering, performance and the integration of performance analysis methodologies gain increasing importance, especially for complex systems. Well-developed methods and tools can predict non-functional performance properties like response time or resource utilization in early design stages, thus promising time and cost savings. However, as performance modeling and performance prediction is still a young research area, the methods are not yet well-established and in wide-spread industrial use. This work is a case study of the applicability of the Palladio Component Model as a performance prediction method in an industrial environment. We model and analyze different design alternatives for storage virtualization on an IBM (Trademark of IBM in USA and/or other countries) system. The model calibration, validation and evaluation is based on data measured on a System z9 (Trademark of IBM in USA and/or other countries) as a proof of concept. The results show that performance predictions can identify performance bottlenecks and evaluate design alternatives in early stages of system development. The experiences gained were that performance modeling helps to understand and analyze a system. Hence, this case study substantiates that performance modeling is applicable in industry and a valuable method for evaluating design decisions.

{Challenges and Opportunities of Cloud Computing -- Trade-off Decisions in Cloud Computing Architecture}. (2010-19), Hauck, Michael; Huber, Matthias; Klems, Markus; Kounev, Samuel; M{ü}ller-Quade, J{ö}rn; Pretschner, Alexander; Reussner, Ralf; Tai, Stefan; (2010). Karlsruhe Institue of Technology, Faculty of Informatics.

[ BibTeX ]
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@techreport{hauck2010b,
  author = {Hauck, Michael and Huber, Matthias and Klems, Markus and Kounev, Samuel and M{\"u}ller-Quade, J{\"o}rn and Pretschner, Alexander and Reussner, Ralf and Tai, Stefan},
  institution = {Karlsruhe Institue of Technology, Faculty of Informatics},
  keywords = {t_bookchapter},
  number = {2010-19},
  title = {{Challenges and Opportunities of Cloud Computing – Trade-off Decisions in Cloud Computing Architecture}},
  year = 2010
}

%0 Report
%1 hauck2010b
%A Hauck, Michael
%A Huber, Matthias
%A Klems, Markus
%A Kounev, Samuel
%A M{ü}ller-Quade, J{ö}rn
%A Pretschner, Alexander
%A Reussner, Ralf
%A Tai, Stefan
%D 2010
%N 2010-19
%T {Challenges and Opportunities of Cloud Computing -- Trade-off Decisions in Cloud Computing Architecture}
%U http://digbib.ubka.uni-karlsruhe.de/volltexte/1000020328

{Performance Modeling of Storage Virtualization}. Huber, Nikolaus; (2009, April). Universit{{ä}}t Karlsruhe (TH), Karlsruhe, Germany.

GFFT Prize

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@mastersthesis{Hu2009-UKA-PerfMod,
  address = {Karlsruhe, Germany},
  author = {Huber, Nikolaus},
  keywords = {Automated_model_learning},
  month = {04},
  note = {<b>GFFT Prize</b>},
  school = {Universit{{\"a}}t Karlsruhe (TH)},
  title = {{Performance Modeling of Storage Virtualization}},
  year = 2009
}

%0 Thesis
%1 Hu2009-UKA-PerfMod
%A Huber, Nikolaus
%C Karlsruhe, Germany
%D 2009
%T {Performance Modeling of Storage Virtualization}

Publications of Nikolaus Huber

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