You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Continuous-Emission Markov Models for Real-Time Applications: Bounding Deadline Miss Probabilities

Fulltext:


Publication Type:

Conference/Workshop Paper

Venue:

29th IEEE Real-Time and Embedded Technology and Applications Symposium

DOI:

10.1109/RTAS58335.2023.00009


Abstract

Probabilistic approaches have gained attention over the past decade, providing a modeling framework that enables less pessimistic analysis of real-time systems. Among the different proposed approaches, Markov chains have been shown effective for analyzing real-time systems, particularly in estimating the pending workload distribution and deadline miss probability. However, the state-of-the-art mainly considered discrete emission distributions without investigating the benefits of continuous ones. In this paper, we propose a method for analyzing the workload probability distribution and bounding the deadline miss probability for a task executing in a Constant Bandwidth Server, where execution times are described by a Markov model with Gaussian emission distributions. The evaluation is performed for the timing behavior of a Kalman filter for Furuta pendulum control. Deadline miss probability bounds are derived with a workload accumulation scheme. The bounds are compared to 1)~measured deadline miss ratios of tasks running under the Linux Constant Bandwidth Server with SCHED_DEADLINE, 2) estimates derived from a Markov Model with discrete-emission distributions (PROSIT), 3) simulation-based estimates, and 4) an estimate assuming independent execution times. The results suggest that the proposed method successfully upper bounds actual deadline miss probabilities. Compared to the discrete-emission counterpart, the computation time is independent of the range of the execution times under analysis, and resampling is not required.

Bibtex

@inproceedings{Friebe6659,
author = {Anna Friebe and Filip Markovic and Alessandro Papadopoulos and Thomas Nolte},
title = {Continuous-Emission Markov Models for Real-Time Applications: Bounding Deadline Miss Probabilities},
pages = {14--26},
month = {May},
year = {2023},
booktitle = {29th IEEE Real-Time and Embedded Technology and Applications Symposium},
url = {http://www.es.mdu.se/publications/6659-}
}