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Electronics 2019, 8(2), 116; https://doi.org/10.3390/electronics8020116

A Task Parameter Inference Framework for Real-Time Embedded Systems

Department of Computer Science and Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Korea
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Received: 21 December 2018 / Revised: 18 January 2019 / Accepted: 19 January 2019 / Published: 22 January 2019
(This article belongs to the Special Issue Real Time Dependable Distributed Control Systems)
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Abstract

While recent studies addressed security attacks in real-time embedded systems, most of them assumed prior knowledge of parameters of periodic tasks, which is not realistic under many environments. In this paper, we address how to infer task parameters, from restricted information obtained by simple system monitoring. To this end, we first develop static properties that are independent of inference results and therefore applied only once in the beginning. We further develop dynamic properties each of which can tighten inference results by feeding an update of the inference results obtained by other properties. Our simulation results demonstrate that the proposed inference framework infers task parameters for RM (Rate Monotonic) with reasonable tightness; the ratio of exactly inferred task periods is 95.3% and 65.6%, respectively with low and high task set use. The results also discover that the inference performance varies with the monitoring interval length and the task set use. View Full-Text
Keywords: task parameter inference; real-time embedded systems; real-time scheduling task parameter inference; real-time embedded systems; real-time scheduling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Jung, N.; Baek, H.; Lee, J. A Task Parameter Inference Framework for Real-Time Embedded Systems. Electronics 2019, 8, 116.

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