Modelling of Input-Parameter Dependency for Performance Predictions of Component-Based Embedded Systems

  • Authors:

  • Egor Bondarev;Peter de With;Michel Chaudron;Johan Muskens

  • Affiliations:

  • System Architecture and Networking1 and Video, Coding and Architectures groups Eindhoven University of Technology, The Netherlands;System Architecture and Networking1 and Video, Coding and Architectures groups Eindhoven University of Technology, The Netherlands;System Architecture and Networking1 and Video, Coding and Architectures groups Eindhoven University of Technology, The Netherlands;System Architecture and Networking1 and Video, Coding and Architectures groups Eindhoven University of Technology, The Netherlands

  • Venue:
  • EUROMICRO '05 Proceedings of the 31st EUROMICRO Conference on Software Engineering and Advanced Applications
  • Year:
  • 2005

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Abstract

The guaranty of meeting the timing constraints during the design phase of real-time component-based embedded software has not been realized. To satisfy real-time requirements, we need to understand behaviour and resource usage of a system over time. In this paper we address both aspects in detail by observing the influence of input data on the system behaviour and performance. We extend an existing scenario simulation approach that features the modelling of input parameter dependencies and simulating the execution of the models. The approach enables specification of the dependencies in the component models, as well as initialisation of the parameters in the application scenario model. This gives a component-based application designer an explorative possibility of going through all possible execution scenarios with different parameter initialisations, and finding the worst-case scenarios where the predicted performance does not satisfy the requirements. The identification of these scenarios is important because it avoids system redesign at the later stage. In addition, the conditional behaviour and resource usage modelling with respect to the input data provide more accurate prediction.