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A Trace-Based Statistical Worst-Case Execution Time Analysis of Component-Based Real-Time Embedded Systems

Fulltext:


Authors:

Yue Lu, Thomas Nolte, Iain Bate, Liliana Cucu-Grosjean

Publication Type:

Conference/Workshop Paper

Venue:

16th IEEE International Conference on Emerging Technology and Factory Automation (ETFA11), Work-in-Progress (WiP) session


Abstract

This paper describes the tool support for a framework for performing statistical WCET analysis of real-time embedded systems by using bootstrapping sampling and Extreme Value Theory (EVT). To be specific, bootstrapping sampling is used to generate timing traces, which not only fulfill the requirements given by statistics and probability theory, but also are robust to use in the context of estimating the WCET of programs. Next, our proposed statistical inference uses EVT to analyze such timing traces, and computes a WCET estimate of the target program, pertaining to a given predictable probability. The evaluation results show that our proposed method could have the potential of being able to provide a tighter upper bound on the WCET estimate of the programs under analysis, when compared to the estimates given by the referenced WCET analysis methods.

Bibtex

@inproceedings{Lu2169,
author = {Yue Lu and Thomas Nolte and Iain Bate and Liliana Cucu-Grosjean},
title = {A Trace-Based Statistical Worst-Case Execution Time Analysis of Component-Based Real-Time Embedded Systems },
month = {September},
year = {2011},
booktitle = {16th IEEE International Conference on Emerging Technology and Factory Automation (ETFA11), Work-in-Progress (WiP) session},
url = {http://www.es.mdh.se/publications/2169-}
}