Minimizing the cardinality of a real-time task set by automated task clustering

Antoine Bertout 1, 2, 3 Julien Forget 1, 2, 3 Richard Olejnik 1, 2, 3
1 DART - Contributions of the Data parallelism to real time
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
2 LIFL - DART/Émeraude
LIFL - Laboratoire d'Informatique Fondamentale de Lille
3 LIFL - Laboratoire d'Informatique Fondamentale de Lille
Abstract : The objective of this paper is first to properly define the notion of task clustering. This is the process of automatically mapping functionalities (blocks of code corresponding to a high-level feature) with real-time constraints to tasks (or threads). We aim at reducing the number of tasks functionalities are mapped to, while preserving the schedulability of the initial system. Second, our goal is to expose the complexity of the problem and to sketch methods we will propose for solving this problem. We consider independent tasks running on a single processor.
Keywords : real-time scheduling task clustering functionality to task mapping
Type de document :
Communication dans un congrès
Proceedings of the 7th Junior Researcher Workshop on Real-Time Computing (JRWRTC 2013), Oct 2013, Sophia Antipolis, France. pp.9-12, 2013
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Antoine Bertout, Julien Forget, Richard Olejnik. Minimizing the cardinality of a real-time task set by automated task clustering. Proceedings of the 7th Junior Researcher Workshop on Real-Time Computing (JRWRTC 2013), Oct 2013, Sophia Antipolis, France. pp.9-12, 2013. 〈hal-00874979〉

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