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Efficiently Bounding Deadline Miss Probabilities of Markov Chain Real-Time Tasks

Fulltext:


Authors:

Anna Friebe, Filip Markovic, Alessandro Papadopoulos, Thomas Nolte

Research group:

Complex Real-Time Embedded Systems

Publication Type:

Journal article

Venue:

Real-Time Systems

DOI:

10.1007/s11241-024-09431-7

Abstract

In real-time systems analysis, probabilistic models, particularly Markov chains, have proven effective for tasks with dependent executions. This paper improves upon an approach utilizing Gaussian emission distributions within a Markov task execution model that analyzes bounds on deadline miss probabilities for tasks in a reservation-based server. Our method distinctly addresses the issue of runtime complexity, prevalent in existing methods, by employing a state merging technique. This not only maintains computational efficiency but also retains the accuracy of the deadline-miss probability estimations to a significant degree. The efficacy of this approach is demonstrated through the timing behavior analysis of a Kalman filter controlling a Furuta pendulum, comparing the derived deadline miss probability bounds against various benchmarks, including real-time Linux server metrics. Our results confirm that the proposed method effectively upper-bounds the actual deadline miss probabilities, showcasing a significant improvement in computational efficiency without significantly sacrificing accuracy.

Bibtex

@article{Friebe7041,
author = {Anna Friebe and Filip Markovic and Alessandro Papadopoulos and Thomas Nolte},
title = {Efficiently Bounding Deadline Miss Probabilities of Markov Chain Real-Time Tasks},
volume = {51},
number = {3},
pages = {1--48},
month = {October},
year = {2024},
journal = {Real-Time Systems},
url = {http://www.es.mdu.se/publications/7041-}
}