Overload Management Through Selective Data Dropping

Flavia Felicioni 1 Ning Jia 1 Françoise Simonot-Lion 1 Ye-Qiong Song 1
1 TRIO - Real time and interoperability
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : During system and network overload periods, excessive delay or even data loss may occur. To maintain the quality of control of an NCS, the implementation system (including both computer and network) overload must be correctly handled. In this chapter, as an alternative to the explicit sampling period adjustment, we present an indirect sampling period adjustment approach which is based on selective sampling data dropping according to the (m, k)-firm model. The interest of this alternative is its easy implementation despite having less adjustment quality, since only the multiples of the basic sampling period can be exploited. Upon overload detection, the basic idea is to selectively drop some samples according to the (m, k)-firm model to avoid long consecutive data drops. The consequence is that the shared network and processor will be less loaded. However, the control stability and performance must still be maintained to an acceptable level. This can be achieved by keeping either the total control tasks on a same processor or the messages sharing a same network bandwidth schedulable under the (m, k)-firm constraint.
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Chapitre d'ouvrage
Christophe Aubrun, Daniel Simon, Ye-Qiong Song. Co-design Approaches - Dependable Networked Control Systems, ISTE - John Wiley, pp.187-224, 2010
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Dernière modification le : jeudi 11 janvier 2018 - 06:20:05
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Flavia Felicioni, Ning Jia, Françoise Simonot-Lion, Ye-Qiong Song. Overload Management Through Selective Data Dropping. Christophe Aubrun, Daniel Simon, Ye-Qiong Song. Co-design Approaches - Dependable Networked Control Systems, ISTE - John Wiley, pp.187-224, 2010. 〈inria-00433112〉

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