Fault-aware sensitivity analysis for probabilistic real-time systems
Luca Santinelli, Zhishan Guo, Laurent George · 2016
In probabilistic real-time modeling, diverse task execution conditions can be characterized with probabilistic distributions, where multiple execution time thresholds are represented, each with an exceeding probability. Comparing to traditional deterministic real-time, probabilistic approaches provide more flexibility in system behavior modeling, which may result in more precise schedulability analysis. With this work, we combine sensitivity analysis and probabilistic models of fault effects on task execution behaviors. The goal is to develop probabilistic schedulability analysis that is applicable to both faulty and non-faulty execution conditions. While the probabilities accurately characterize faults and faults effects on worst-case execution times, the probabilistic schedulability analysis both qualifies and quantifies faults impacts on system schedulability.