Monte Carlo Response-Time Analysis

Determining a soft or firm real-time task's probabilistic worst-case response time is a central goal when quantifying and bounding the probability of deadline misses, but current approaches are either (i) fast, but coarse-grained analytical bounds without precision guarantees, (ii) based on convolution and suffer from high space and time complexity, or (iii) combine convolution with resampling techniques that accrue pessimism in an uncontrolled manner. As a new alternative, this paper provides the first probabilistic response-time analysis method based on Monte Carlo simulation, which provides a controlled trade-off between analysis runtime, the desired degree of accuracy, and the permissible probability of a misestimate. An evaluation shows the proposed Monte Carlo analysis to routinely provide more accurate worst-case deadline failure probability (WCDFP) estimates than prior approaches, especially when considering large task sets (where prior methods struggle). In particular, it is shown to scale to workloads with up to 500 tasks while achieving one to three orders of magnitude better precision than analytical or convolution-based approaches (given an equivalent time budget).