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

Friebe, Anna; Markovic, Filip; Papadopoulos, Alessandro Vittorio; Nolte, Thomas

doi:10.1109/rtas58335.2023.00009

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Efficiently bounding deadline miss probabilities of Markov chain real-time tasks

Friebe,

Marković,

Papadopoulos

et al. 2024

Real-Time Syst

View full text Add to dashboard Cite

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.

show abstract