A mixed model for estimating the probabilistic worst case execution time

Cristian Maxim 1, 2 Adriana Gogonel 1 Liliana Cucu-Grosjean 1
1 AOSTE - Models and methods of analysis and optimization for systems with real-time and embedding constraints
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt, Laboratoire I3S - COMRED - COMmunications, Réseaux, systèmes Embarqués et Distribués
Abstract : Probabilistic analysis are considered nowadays an interesting solution for real-time systems as the probability of appearance of worst-case values is small (10−45 per hour of functioning) compared to the accepted probability of failure (10−9 per hour of functioning for the highest safety level in avionics). In order to take into account this information, Burns and Edgar [1] have introduced the notion of probabilistic worst case execution time (pWCET). The pWCET of a program is bounding the probability that the execution time of that program exceeds a given value. A possible method to estimate the pWCET is based on measurements and the associated analysis is called measurement-based probabilistic timing analysis (MBPTA). Such method has been proposed by Cucu-Grosjean et al. [2] and the obtained estimate is sensitive to the observed execution times. To our best knowledge this dependence of MBPTA on the observations is an open problem. Within this paper we propose a first solution based on a mixed model using genetic algorithms.
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Cristian Maxim, Adriana Gogonel, Liliana Cucu-Grosjean. A mixed model for estimating the probabilistic worst case execution time. Seminar Open Problems in real-time computing (OPRT-ULB 2012), Apr 2014, Bruxelles, Belgium. ⟨hal-01298725⟩



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