Schedulability analysis of dependent probabilistic real-time tasks

Slim Ben-Amor 1 Dorin Maxim 2 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 , Laboratoire I3S - COMRED - COMmunications, Réseaux, systèmes Embarqués et Distribués, Inria de Paris
2 MADYNES - Management of dynamic networks and services
Inria Nancy - Grand Est, LORIA - NSS - Department of Networks, Systems and Services
Abstract : The complexity of modern architectures has increased the timing variability of programs (or tasks). In this context, new approaches based on probabilistic methods are proposed to decrease the pessimism by associating probabilities to the worst case values of the programs (tasks) time execution. In this paper, we extend the original work of Chetto et al. [7] on precedence constrained tasks to the case of tasks with worst case execution times described by probability distributions. The precedence constraints between tasks are defined by acyclic directed graphs and these constraints are transformed in appropriate release times and deadlines. The new release times and deadlines are built using new maximum and minimum relations between pairs of probability distributions. We provide a probabilistic schedulability condition based on these new relations.
Type de document :
Communication dans un congrès
RTNS 2016 - 24th International Conference on Real-Time Networks and Systems, Oct 2016, Brest, France. ACM DL, pp.99-107, 2016, 〈10.1145/2997465.2997499〉
Liste complète des métadonnées
https://hal.inria.fr/hal-01419741
Contributeur : Liliana Cucu-Grosjean <>
Soumis le : lundi 19 décembre 2016 - 19:24:06
Dernière modification le : mardi 18 décembre 2018 - 16:26:02

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Slim Ben-Amor, Dorin Maxim, Liliana Cucu-Grosjean. Schedulability analysis of dependent probabilistic real-time tasks. RTNS 2016 - 24th International Conference on Real-Time Networks and Systems, Oct 2016, Brest, France. ACM DL, pp.99-107, 2016, 〈10.1145/2997465.2997499〉. 〈hal-01419741〉

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