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Browsing by Author "Reghenzani, Federico"

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  • Mixed Criticality Scheduling of Probabilistic Real-Time Systems
    Publication . Singh, Jasdeep; Santinelli, Luca; Reghenzani, Federico; Bletsas, Konstantinos; Doose, David; Guo, Zhishan
    In this paper we approach the problem of Mixed Criticality (MC) for probabilistic real-time systems where tasks execution times are described with probabilistic distributions. In our analysis, the task enters high criticality mode if its response time exceeds a certain threshold, which is a slight deviation from a more classical approach in MC. We do this to obtain an application oriented MC system in which criticality mode changes depend on actual scheduled execution. This is in contrast to classical approaches which use task execution time to make criticality mode decisions, because execution time is not affected by scheduling while the response time is. We use a graph-based approach to seek for an optimal MC schedule by exploring every possible MC schedule the task set can have. The schedule we obtain minimizes the probability of the system entering high criticality mode. In turn, this aims at maximizing the resource efficiency by the means of scheduling without compromising the execution of the high criticality tasks and minimizing the loss of lower criticality functionality. The proposed approach is applied to test cases for validation purposes.
    2019Conference object Restricted access Show more
  • Non-Preemptive Scheduling of Periodic Mixed-Criticality Real-Time Systems
    Publication . Singh, Jasdeep; Santinelli, Luca; Reghenzani, Federico; Bletsas, Konstantinos; Guo, Zhishan
    In this work we develop an offline analysis of periodic mixed-criticality real-time systems. We develop a graph-based exploratory method to non-preemptively schedule multiple criticality tasks. The exploration process obtains a schedule for each periodic instance of the tasks. The schedule adjusts for criticality mode changes to maximize the resource usage by allowing lower criticality executions. At the same time, it ensures that the schedulability of other higher criticality jobs is never compromised. We also quantify the probabilities associated to a criticality mode change by using task probabilistic Worst Case Execution Times. A method to reduce the offline complexity is also proposed.
    2020Conference object Open access Show more