Modelling Layered Component Execution Environments for Performance Prediction

  • Authors:

  • Michael Hauck;Michael Kuperberg;Klaus Krogmann;Ralf Reussner

  • Affiliations:

  • FZI Research Center for Information Technology, Karlsruhe, Germany;Software Design and Quality Group, Universität Karlsruhe (TH), Germany;Software Design and Quality Group, Universität Karlsruhe (TH), Germany;Software Design and Quality Group, Universität Karlsruhe (TH), Germany

  • Venue:
  • CBSE '09 Proceedings of the 12th International Symposium on Component-Based Software Engineering
  • Year:
  • 2009

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Abstract

Software architects often use model-based techniques to analyse performance (e.g. response times), reliability and other extra-functional properties of software systems. These techniques operate on models of software architecture and execution environment, and are applied at design time for early evaluation of design alternatives, especially to avoid implementing systems with insufficient quality. Virtualisation (such as operating system hypervisors or virtual machines) and multiple layers in execution environments (e.g. RAID disk array controllers on top of hard disks) are becoming increasingly popular in reality and need to be reflected in the models of execution environments. However, current component meta-models do not support virtualisation and cannot model individual layers of execution environments. This means that the entire monolithic model must be recreated when different implementations of a layer must be compared to make a design decision, e.g. when comparing different Java Virtual Machines. In this paper, we present an extension of an established model-based performance prediction approach and associated tools which allow to model and predict state-of-the-art layered execution environments, such as disk arrays, virtual machines, and application servers. The evaluation of the presented approach shows its applicability and the resulting accuracy of the performance prediction while respecting the structure of the modelled resource environment.