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Application of Advanced Model-Driven Techniques in Performance Engineering

  • Lucia Kapova
  • Ralf Reussner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6342)

Abstract

Software performance engineering supports software architects to identify potential performance problems, such as bottlenecks, in their software systems during the design phase. In such early stages of the software life-cycle, only little information is available about the system’s implementation and execution environment. However, these details are crucial for accurate performance predictions. Performance completions close the gap between available high-level models and required low-level details. Using model-driven technologies, transformations can include details of the implementation and execution environment into abstract performance models. Existing approaches do not consider the relation of actual implementations and performance models used for prediction. Furthermore, they neglect the broad variety of implementations and middleware platforms, possible configurations, and varying usage scenarios. To allow more accurate performance predictions, we extend classical performance engineering by automated model refinements based on a library of reusable performance completions. We use model-driven techniques, more specifically higher-order transformations, to implement and automatically integrate performance completions in the context of the Palladio Component Model. With our tool set, software architects can model an application in a language specific to their domain. They can annotate the model elements that require further refinement. Higher-order transformations then apply the selected completion with its configuration. In a case study of a middleware configuration, we illustrate the benefit of performance completions with respect to the accuracy of performance predictions.

Keywords

Model Transformation Object Management Group Mark Model Performance Completion Palladio Component Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lucia Kapova
    • 1
  • Ralf Reussner
    • 1
  1. 1.Software Design and Quality GroupKarlsruhe Institute of Technology (KIT)Germany

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