Efficient and robust probabilistic guarantees for real-time tasks

This paper presents a new method for providing probabilistic real-time guarantees to tasks scheduled through resource reservations. Previous work on probabilistic analysis of reservation-based schedulers is extended by improving the efficiency and robustness of the probability computation. Robustness is improved by accounting for a possibly incomplete knowledge of the distribution of the computation times (which is typical in realistic applications). The proposed approach computes a conservative bound for the probability of missing deadlines, based on the knowledge of the probability distributions of the execution times and of the inter-arrival times of the tasks. In this paper, such a bound is computed in realistic situations, comparing it with simulative results and with the exact computation of deadline miss probabilities (without pessimistic bounds). Finally, the impact of the incomplete knowledge of the execution times distribution is evaluated.

► Stochastic model of a real–time task scheduled through a resource reservation. ► Improvements respect to previous work: efficiency and robustness. ► Numerically efficient technique for computing the probability of a deadline miss ► Robustness with respect to incomplete knowledge of the distributions. ► Compared with previous work and with simulations through and extensive set of experiments.