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Identification of execution modes for real-time systems using cluster analysis

Abstract : Estimating bounds for the execution or response times of a task is a central concern for real-time designers. Several solutions exist, and probabilistic approaches estimate such bounds by building appropriate probability distributions. Those methods are safe, but they may be pessimistic and rely on strong hypothesis such as independence between tasks. The worst case execution times of tasks are hard to estimate because their measurements are usually disturbed by the system itelf. In general measures are done in isolation, however dependencies between tasks are rarely modeled in that case. By isolation, we mean that a task (or program) is executed without any type of interference coming from other executed tasks. In this paper we propose a statistical analysis of measured response times based on clustering analysis, i.e., building classes of response times that may identify executing modes for a given set of tasks. This work is a first step towards a multivariate analysis that may explicitly identify dependence structures.
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Submitted on : Tuesday, September 15, 2020 - 11:13:50 AM
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Kevin Zagalo, Liliana Cucu-Grosjean, Avner Bar-Hen. Identification of execution modes for real-time systems using cluster analysis. 25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Sep 2020, Vienne, Austria. ⟨10.1109/ETFA46521.2020.9211983⟩. ⟨hal-02938202⟩



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