Summary In recent years, series of important achievements have paved the way for the introduction of probabilistic analysis in the area of soft real‐time systems design. In this article, we present an extensible design tool, called PROSIT, which facilitates the access to this technology for a potentially large number of researchers and industrial practitioners. The tool enables the probabilistic analysis of the temporal performance of a real‐time task under fixed‐priority and resource reservations scheduling algorithms. For resource reservations, the tool also offers an automatic procedure for the synthesis of scheduling parameters that optimize a quality metric related to the probabilistic behavior of the tasks.

A large class of modern real-time applications exhibits important variations in the computation time and is resilient to occasional deadline misses. In such cases, probabilistic methods, in which the probability of a deadline miss can be guaranteed and related to the scheduling design choices, can be an important tool for system design. Several techniques for probabilistic guarantees exist for the resource reservation scheduler and are based on the assumption that the process describing the application is independent and identically distributed (i.i.d.). In this paper, we consider a particular class of robotic application for which this assumption is not verified. For such applications, we have verified that the computation time is more faithfully described by a Markov model. We propose techniques based on the theory of hidden Markov models to extract the structure of the model from the observation of a number of execution traces of the application. As a second contribution, we show how to adapt probabilistic guarantees to a Markovian computation time. Our experimental results reveal a very good match between the theoretical findings and the experiments.

We show a methodology for the computation of the probability of deadline miss for a periodic real-time task scheduled by a resource reservation algorithm. We propose a modelling technique for the system that reduces the computation of such a probability to that of the steady state probability of an infinite state Discrete Time Markov Chain with a periodic structure. This structure is exploited to develop an efficient numeric solution where different accuracy/computation time trade-offs can be obtained by operating on the granularity of the model. More importantly we offer a closed form conservative bound for the probability of a deadline miss. Our experiments reveal that the bound remains reasonably close to the experimental probability in one real-time application of practical interest. When this bound is used for the optimisation of the overall Quality of Service for a set of tasks sharing the CPU, it produces a good sub-optimal solution in a small amount of time.