A Survey of Probabilistic Timing Analysis Techniques for Real-Time Systems

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.

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A Survey of Probabilistic Timing Analysis Techniques for Real-Time Systems

Cited by 9 publications

References 102 publications

Turna: a control flow graph reconstruction tool for RISC-V architecture

Turna: a control flow graph reconstruction tool for RISC-V architecture

On the impact of hardware-related events on the execution of real-time programs

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

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