Sensitivity analysis of complex embedded real-time systems

  • Authors:

  • Razvan Racu;Arne Hamann;Rolf Ernst

  • Affiliations:

  • Institute of Computer and Communication Network Engineering, Technical University of Braunschweig, Braunschweig, Germany 38106;Institute of Computer and Communication Network Engineering, Technical University of Braunschweig, Braunschweig, Germany 38106;Institute of Computer and Communication Network Engineering, Technical University of Braunschweig, Braunschweig, Germany 38106

  • Venue:
  • Real-Time Systems
  • Year:
  • 2008

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Abstract

The robustness of an architecture to changes is a major concern in the design of efficient and reliable state-of-the-art embedded real-time systems. Robustness is important during design process to identify if and in how far a system can accommodate later changes or updates, or whether it can be reused in a next generation product. In the product life-cycle, robustness helps the designer to perform changes as a result of product updates, integration of new components and subsystems, or modifications of the environment. In this paper we determine robustness as a performance reserve, the slack in performance before a system fails to meet timing requirements. This is measured as design sensitivity. Due to complex component interactions, resource sharing and functional dependencies, one-dimensional sensitivity analysis might not cover all effects that modifications of one system property may have on system performance. One reason is that the variation of one property can also affect the values of other system properties requiring new approaches to keep track of simultaneous parameter changes. In this paper we present a framework for one-dimensional and multi-dimensional sensitivity analysis of real-time systems. The framework is based on compositional analysis that is scalable to large systems. The one-dimensional sensitivity analysis combines a binary search technique with a set of formal equations derived from the real-time scheduling theory. The multi-dimensional sensitivity analysis engine consists of an exact algorithm that extends the one-dimensional approach, and a stochastic algorithm based on evolutionary search techniques.