Schedulability of Herschel-Planck Revisited Using Statistical Model Checking
Schedulability analysis is a main concern for several embedded applications due to their safety-critical nature. The classical method of response time analysis provides an efficient technique used in industrial practice. However, the method is based on conservative assumptions related to execution and blocking times of tasks. Consequently, the method may falsely declare deadline violations that will never occur during execution. This paper is a continuation of previous work of the authors in applying extended timed automata model checking (using the tool UPPAAL) to obtain more exact schedulability analysis, here in the presence of non-deterministic computation times of tasks given by intervals [BCET,WCET]. Considering computation intervals makes the schedulability of the resulting task model undecidable. Our contribution is to propose a combination of model checking techniques to obtain some guarantee on the (un)schedulability of the model even in the presence of undecidability.
Two methods are considered: symbolic model checking and statistical model checking. Symbolic model checking allows to conclude schedulability – i.e. absence of deadline violations – for varying sizes of BCET. However, the symbolic model checking technique is over-approximating for the considered task model and can therefore not be used for disproving schedulability. As a remedy, we show how statistical model checking may be used to generate concrete counter examples witnessing non-schedulability. In addition, we apply statistical model checking to obtain more informative performance analysis – e.g. expected response times – when the system is schedulable.
The methods are demonstrated on a complex satellite software system yielding new insights useful for the company.
KeywordsModel Check Response Time Distribution Schedulability Analysis Symbolic Model Check Response Time Analysis
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