article_kuperberg.bib

@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 = {Michael Hauck and Michael Kuperberg and Nikolaus Huber and Ralf Reussner},
  doi = {10.1007/s10270-013-0335-7},
  issn = {1619-1366},
  journal = {Software \& Systems Modeling},
  pages = {1-21},
  publisher = {Springer-Verlag},
  title = {Deriving performance-relevant infrastructure properties through model-based experiments with Ginpex},
  url = {http://dx.doi.org/10.1007/s10270-013-0335-7},
  year = {2013}
}

@article{krogmann2009c,
  abstract = {In component-based software engineering, existing components are often re-used in new applications. Correspondingly, the response time of an entire component-based application can be predicted from the execution durations of individual component services. These execution durations depend on the runtime behaviour of a component, which itself is influenced by three factors: the execution platform, the usage profile, and the component wiring. To cover all relevant combinations of these influencing factors, conventional prediction of response times requires repeated deployment and measurements of component services for all such combinations, incurring a substantial effort. This paper presents a novel comprehensive approach for reverse engineering and performance prediction of components. In it, genetic programming is utilised for reconstructing a behaviour model from monitoring data, runtime bytecode counts and static bytecode analysis. The resulting behaviour model is parametrised over all three performance-influencing factors, which are specified separately. This results in significantly fewer measurements: the behaviour model is reconstructed only once per component service, and one application-independent bytecode benchmark run is sufficient to characterise an execution platform. To predict the execution durations for a concrete platform, our approach combines the behaviour model with platform-specific benchmarking results. We validate our approach by predicting the performance of a file sharing application.},
  author = {Klaus Krogmann and Michael Kuperberg and Ralf Reussner},
  doi = {http://doi.ieeecomputersociety.org/10.1109/TSE.2010.69},
  editor = {Mark Harman and Afshin Mansouri},
  issn = {0098-5589},
  journal = {IEEE Transactions on Software Engineering},
  number = {6},
  pages = {865--877},
  publisher = {{IEEE}},
  title = {{Using Genetic Search for Reverse Engineering of Parametric Behaviour Models for Performance Prediction}},
  url = {http://sdqweb.ipd.kit.edu/publications/pdfs/krogmann2009c.pdf},
  volume = {36},
  year = {2010}
}