article_hubern.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{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 = {Nikolas Roman Herbst and Nikolaus Huber and Samuel Kounev and Erich Amrehn},
doi = {10.1002/cpe.3224},
issn = {1532-0634},
journal = {Concurrency and Computation - Practice and Experience, Special Issue with extended versions of the best papers from ICPE 2013, John Wiley and Sons, Ltd.},
keywords = {workload forecasting, arrival rate, time series analysis, proactive resource provisioning, assurance of service level objectives},
title = {{Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}},
url = {http://dx.doi.org/10.1002/cpe.3224},
year = {2014}
}
@article{Thomas20111,
address = {Amsterdam, The Netherlands},
author = {Nigel Thomas and Jeremy Bradley and William Knottenbelt and Samuel Kounev and Nikolaus Huber and Fabian Brosig},
doi = {10.1016/j.entcs.2011.09.001},
issn = {1571-0661},
journal = {Electronic Notes in Theoretical Computer Science},
pages = {1 - 3},
publisher = {Elsevier Science Publishers B. V.},
title = {Preface},
volume = {275},
year = {2011}
}
@article{BrHuKo2013-SciCo-SoftwarePerformanceAbstractions,
author = {Fabian Brosig and Nikolaus Huber and Samuel Kounev},
doi = {10.1016/j.scico.2013.06.004},
journal = {Elsevier Science of Computer Programming Journal (SciCo)},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/BrHuKo2013-SciCo-SoftwarePerformanceAbstractions.pdf},
publisher = {Elsevier},
title = {{Architecture-Level Software Performance Abstractions for Online Performance Prediction}},
url = {http://authors.elsevier.com/sd/article/S0167642313001421},
year = {2014},
volume = {90, Part B},
pages = {71 - 92},
year = {2014},
issn = {0167-6423}
}
@article{HuHoKoBrKo2014-SOCA-ModelingRuntimeAdaptation,
author = {Nikolaus Huber and Andr\'{e} van Hoorn and Anne Koziolek and Fabian Brosig and Samuel Kounev},
doi = {10.1007/s11761-013-0144-4},
journal = {Service Oriented Computing and Applications Journal (SOCA)},
number = {1},
pages = {73--89},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/HuHoKoBrKo2013-SOCA-ModelingRuntimeAdaptation.pdf},
publisher = {Springer London},
title = {{Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments}},
volume = {8},
year = {2014},
tags = {peer-reviewed}
}
@article{huber2017a,
author = {Huber, Nikolaus and Brosig, Fabian and Spinner, Simon and Kounev, Samuel and B{\"a}hr, Manuel},
title = {{Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language}},
year = {2017},
volume = {43},
number = {5},
journal = {IEEE Transactions on Software Engineering (TSE)},
publisher = {IEEE Computer Society},
doi = {10.1109/TSE.2016.2613863},
pdf = {http://se2.informatik.uni-wuerzburg.de/pa/publications/download/paper/1143.pdf}
}