article_groenda.bib

@article{groenda2009,
  author = {Henning Groenda and Christoph Rathfelder and Ralph Mueller},
  journal = {Eclipse Magazine},
  month = {March},
  pages = {8--10},
  timestamp = {2009-04-02},
  title = {{Best of Eclipse DemoCamps - Ein Erfahrungsbericht vom dritten Karlsruher Eclipse DemoCamp}},
  volume = {3},
  year = {2009}
}

@article{hinkel2016f,
  abstract = {{Bio-inspired robots still rely on classic robot control although advances in neurophysiology allow adaptation to control as well. However, the connection of a robot to spiking neuronal networks needs adjustments for each purpose and requires frequent adaptation during an iterative development. Existing approaches cannot bridge the gap between robotics and neuroscience or do not account for frequent adaptations. The contribution of this paper is an architecture and domain-specific language (DSL) for connecting robots to spiking neuronal networks for iterative testing in simulations, allowing neuroscientists to abstract from implementation details. The framework is implemented in a web-based platform. We validate the applicability of our approach with a case study based on image processing for controlling a four-wheeled robot in an experiment setting inspired by Braitenberg vehicles.}},
  author = {Hinkel, Georg and Groenda, Henning and Krach, Sebastian and Vannucci, Lorenzo and Denninger, Oliver and Cauli, Nino and Ulbrich, Stefan and Roennau, Arne and Falotico, Egidio and Gewaltig, Marc-Oliver and Knoll, Alois and Dillmann, R\"udiger and Laschi, Cecilia and Reussner, Ralf},
  issn = {0921-0296},
  journal = {Journal of Intelligent \& Robotics Systems},
  title = {{A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks}},
  publisher = {Springer},
  year = {2016},
  tags = {refereed},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/hinkel2016f.pdf}
}

@article{rathfelder2008c,
  address = {Bonn, Germany},
  author = {Christoph Rathfelder and Henning Groenda},
  journal = {Softwaretechnik-Trends},
  month = {November},
  number = {4},
  pages = {3--7},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/rathfelder2008c.pdf},
  publisher = {GI (Gesellschaft fuer Informatik)},
  timestamp = {2009.01.19},
  title = {{T}owards an {A}rchitecture {M}aintainability {M}aturity {M}odel ({AM}3)},
  volume = {28},
  year = {2008}
}

@article{KiHeKo-TAAS17-ModelExtractLoadProfiles,
  author = {J\'oakim von Kistowski and Nikolas Herbst and Samuel Kounev and Henning Groenda and Christian Stier and Stebastian Lehrig},
  title = {{Modeling and Extracting Load Intensity Profiles}},
  journal = {ACM Transactions on Autonomous and Adaptive Systems (TAAS)},
  issue_date = {2017},
  year = {2017},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {Load Intensity Variation, Load Profile, Open Workloads, Meta-Modeling, Transformation, Model Extraction},
  abstract = {Today's system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Autonomic controllers, e.g., an advanced auto-scaling mechanism in a cloud computing context, can benefit from an abstracted load model as knowledge to reconfigure on time and precisely. Existing workload characterization approaches have limited support to capture variations the inter-arrival times of incoming work units over time (i.e., a variable load profile). For example, industrial and scientific benchmarks support constant or stepwise increasing load, or inter-arrival times defined by statistical distributions or recorded traces. These options show shortcomings either in representative character of load variation patterns or in abstraction and flexibility of their format. In this article, we present the Descartes Load Intensity Model (DLIM) approach addressing these issues. DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance is a compact formal description of a load intensity trace. DLIM-based tools provide features for benchmarking, performance and recorded load intensity trace analysis. As manually obtaining and maintaining DLIM instances becomes time consuming, we contribute three automated extraction methods and devised metrics for comparison and method selection. We discuss how these features are used to enhance system management approaches for adaptations during run-time, and how they are integrated into simulation contexts and enable benchmarking of elastic or adaptive behavior. We show that automatically extracted DLIM instances exhibit an average modeling error of 15.2\% over ten different real-world traces that cover between two weeks and seven months. These results underline DLIM model expressiveness. In terms of accuracy and processing speed, our proposed extraction methods for the descriptive models are comparable to existing time series decomposition methods. Additionally, we illustrate DLIM applicability by outlining approaches of workload modeling in systems engineering that employ or rely on our proposed load intensity modeling formalism.},
  note = {{To Appear}}
}