Performance Prediction for Black-Box Components Using Reengineered Parametric Behaviour Models

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Michael Kuperberg and Klaus Krogmann and 
                 Ralf Reussner",
  title =        "Performance Prediction for Black-Box Components Using
                 Reengineered Parametric Behaviour Models",
  booktitle =    "11th International Symposium on Component-Based
                 Software Engineering, CBSE 2008",
  year =         "2008",
  editor =       "Michel R. V. Chaudron and Clemens Szyperski and 
                 Ralf Reussner",
  volume =       "5282",
  series =       "Lecture Notes in Computer Science",
  pages =        "48--63",
  address =      "Karlsruhe, Germany",
  month =        oct # " 14-17",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  isbn13 =       "978-3-540-87890-2",
  DOI =          "doi:10.1007/978-3-540-87891-9_4",
  size =         "16 pages",
  abstract =     "In component-based software engineering, the response
                 time of an entire application is often predicted from
                 the execution durations of individual component
                 services. However, these execution durations are
                 specific for an execution platform (i.e. its resources
                 such as CPU) and for a usage profile. Reusing an
                 existing component on different execution platforms up
                 to now required repeated measurements of the concerned
                 components for each relevant combination of execution
                 platform and usage profile, leading to high effort.
                 This paper presents a novel integrated approach that
                 overcomes these limitations by reconstructing behaviour
                 models with platform-independent resource demands of
                 bytecode components. The reconstructed models are
                 parametrised over input parameter values. Using
                 platform-specific results of bytecode benchmarking, our
                 approach is able to translate the platform-independent
                 resource demands into predictions for execution
                 durations on a certain platform. We validate our
                 approach by predicting the performance of a file
                 sharing application.",

Genetic Programming entries for Michael Kuperberg Klaus Krogmann Ralf H Reussner