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Probabilistic Estimation of Response Times Through Large Deviations

Abstract : We apply large deviation theory to assess the probability that the average, or the sum, of the response times of a sequence of consecutive aperiodic jobs is below a given threshold. This coarse-grained performance metric is for instance adapted to evaluate the responsiveness of a soft-real system or the freshness of input data consumed by an algorithm. The technique proposed works with distribution of response times as input but does not require that the distribution obeys a closed-form equation. Indeed, it can accept empirical distributions given under the form of frequency histograms obtained, for instance, by monitoring the system. Future work should be devoted to further assess the applicability of the proposal and relax some technical assumptions.
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https://hal.inria.fr/inria-00191163
Contributor : Nicolas Navet <>
Submitted on : Tuesday, November 27, 2007 - 4:18:31 PM
Last modification on : Friday, February 26, 2021 - 3:28:07 PM
Long-term archiving on: : Monday, April 12, 2010 - 4:56:43 AM

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  • HAL Id : inria-00191163, version 1

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Nicolas Navet, Liliana Cucu, René Schott. Probabilistic Estimation of Response Times Through Large Deviations. Work-in Progress of the 28th IEEE Real-Time Systems Symposium (RTSS'2007 WiP), Dec 2007, Tucson, United States. ⟨inria-00191163⟩

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