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Conference papers

Scheduling Multi-Periodic Mixed-Criticality DAGs on Multi-Core Architectures

Abstract : Thanks to Mixed-Criticality (MC) scheduling, high and low-criticality tasks can share the same execution platform, improving considerably the usage of computation resources. Even if the execution platform is shared with low-criticality tasks, deadlines of high-criticality tasks must be respected. This is usually enforced thanks to operational modes of the system: if necessary, a high-criticality execution mode allocates more time to high-criticality tasks at the expense of low-criticality tasks' execution. Nonetheless, most MC scheduling policies in the literature have only considered independent task sets. For safety-critical real-time systems, this is a strong limitation: models used to describe reactive safety-critical software often consider dependencies among tasks or jobs. In this paper, we define a meta-heuristic to schedule multi-processor systems composed of multi-periodic Directed Acyclic Graphs of MC tasks. This meta-heuristic computes the scheduling of the system in the high-criticality mode first. The computation of the low-criticality scheduling respects a condition on high-criticality tasks' jobs, ensuring that high-criticality tasks never miss their deadlines. Two implementations of this meta-heuristic are presented. In high-criticality mode, high-criticality tasks are scheduled as late as possible. Then two global scheduling tables are produced, one per criticality mode. Experimental results demonstrate our method outperforms approaches of the literature in terms of acceptance rate for randomly generated systems.
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https://hal.archives-ouvertes.fr/hal-01994627
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Submitted on : Friday, January 25, 2019 - 3:40:42 PM
Last modification on : Tuesday, May 17, 2022 - 6:50:02 PM

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Roberto Medina, Etienne Borde, Laurent Pautet. Scheduling Multi-Periodic Mixed-Criticality DAGs on Multi-Core Architectures. 39th IEEE Real-Time Systems Symposium (RTSS 2018), Dec 2018, Nashville, TN, United States. ⟨10.1109/RTSS.2018.00042⟩. ⟨hal-01994627⟩

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