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In recent years, the interest in probabilistic real-time has grown, as a response to the limitations of traditional static Worst-Case Execution Time (WCET) methods, in performing timing analysis of applications running on complex systems, like multi/many-cores and COTS platforms. The probabilistic theory can partially solve this problem, but it requires strong guarantees on the execution time traces, in order to provide safe probabilistic-WCET estimations. These requirements can be verified through suitable statistical tests, as described in this paper. In this work, we identify also challenges and problems of using statistical testing procedures in probabilistic real-time computing, proposing a unified test procedure based on a single index called Probabilistic Predictability Index (PPI). An experimental campaign has been carried out, considering both synthetic and realistic datasets, and the analysis of the impact of the Linux PREEMPT_RT patch on a modern complex platform as a use-case of the proposed index.

Accurate timing prediction for software execution is becoming a problem due to the increasing complexity of computer architecture, and the presence of mixed-criticality workloads. Probabilistic caches were proposed to set bounds to Worst Case Execution Time (WCET) estimates and help designers improve system resource usage. However, as technology scales down, system fault rates increase and timing behavior is affected. In this paper, we propose a Static Probabilistic Timing Analysis (SPTA) approach for caches with evict-on-miss random replacement policy using a state space modeling technique, with consideration of fault impacts on both timing analysis and task WCET. Different scenarios of transient and permanent faults are investigated. Results show that our proposed approach provides tight probabilistic WCET (pWCET) estimates and as fault rate increases, the timing behavior of the system can be affected significantly.