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Model-Driven Generation of Performance Prototypes

  • Conference paper
Performance Evaluation: Metrics, Models and Benchmarks (SIPEW 2008)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5119))

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Abstract

Early, model-based performance predictions help to understand the consequences of design decisions on the performance of the resulting system before the system’s implementation becomes available. While this helps reducing the costs for redesigning systems not meeting their extra-functional requirements, performance prediction models have to abstract from the full complexity of modern hard- and software environments potentially leading to imprecise predictions. As a solution, the construction and execution of prototypes on the target execution environment gives early insights in the behaviour of the system under realistic conditions. In literature several approaches exist to generate prototypes from models which either generate code skeletons or require detailed models for the prototype. In this paper, we present an approach which aims at automated generation of a performance prototype based solely on a design model with performance annotations. For the concrete realisation, we used the Palladio Component Model (PCM), which is a component-based architecture modelling language supporting early performance analyses. For a typical three-tier business application, the resulting Java EE code shows how the prototype can be used to evaluate the influence of complex parts of the execution environment like memory interactions or the operating system’s scheduler.

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Samuel Kounev Ian Gorton Kai Sachs

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Becker, S., Dencker, T., Happe, J. (2008). Model-Driven Generation of Performance Prototypes. In: Kounev, S., Gorton, I., Sachs, K. (eds) Performance Evaluation: Metrics, Models and Benchmarks. SIPEW 2008. Lecture Notes in Computer Science, vol 5119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69814-2_7

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  • DOI: https://doi.org/10.1007/978-3-540-69814-2_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69813-5

  • Online ISBN: 978-3-540-69814-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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