Speaker

Anna Friebe, Ph.D., Malardalen University

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.

Bio

Anna Friebe is a PhD student at Mälardalen University, Västerås, Sweden since 2019. The PhD project relates to probabilistic tools for analysis and scheduling of real-time systems. She received her MSc in Applied Physics and Electrical Engineering from Linköping University, Sweden in 1998. Anna has a background as a software engineer in the fields of medical image processing, treatment planning systems, and 3D graphics/ haptics simulation, and as a project manager for an autonomous sailboat project at Åland University of Applied Sciences, Mariehamn, Finland 2015-2019.