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A Probabilistic Approach for Fault Tolerant Multiprocessor Real-time Scheduling

Identifieur interne : 005413 ( Main/Merge ); précédent : 005412; suivant : 005414

A Probabilistic Approach for Fault Tolerant Multiprocessor Real-time Scheduling

Auteurs : Vandy Berten ; Goossens Joël ; Emmanuel Jeannot [France]

Source :

RBID : Hal:inria-00097248

English descriptors

Abstract

In this paper we tackle the problem of scheduling a periodic real time system on identical multiprocessor platforms, moreover the tasks considered may fail with a given probability. For each task we compute its duplication rate in order to (1) given a maximum tolerated probability of failure, minimize the size of the platform such at least one replica of each job meets its deadline (and does not fail) using a variant of EDF namely EDF/sup (k)/ or (2) given the size of the platform, achieve the best possible reliability with the same constraints. Thanks to our probabilistic approach, no assumption is made on the number of failures which can occur. We propose several approaches to duplicate tasks and we show that we are able to find solutions always very close to the optimal one.

Url:
DOI: 10.1109/IPDPS.2006.1639409

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Hal:inria-00097248

Le document en format XML

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<div type="abstract" xml:lang="en">In this paper we tackle the problem of scheduling a periodic real time system on identical multiprocessor platforms, moreover the tasks considered may fail with a given probability. For each task we compute its duplication rate in order to (1) given a maximum tolerated probability of failure, minimize the size of the platform such at least one replica of each job meets its deadline (and does not fail) using a variant of EDF namely EDF/sup (k)/ or (2) given the size of the platform, achieve the best possible reliability with the same constraints. Thanks to our probabilistic approach, no assumption is made on the number of failures which can occur. We propose several approaches to duplicate tasks and we show that we are able to find solutions always very close to the optimal one.</div>
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