We apply interval-based timing constraint satisfaction probability results to predict timing constraint violations in real-time embedded system with a known hardware transient failure model. A previous study indicated that hardware transient failures follow a Poisson distribution with an average failure arrival rate λ. Under this model, the distribution of time intervals between successive failures follows an exponential distribution with the same parameter λ Our goal is to use the statistical transient failure models to calculate the earliest time at which we can predict, with a determined level of confidence, that a given timing constraint may be violated. This earlier prediction provides time-critical systems with valuable time before the deadline is reached to adapt themselves, and hence, to minimize possible negative impacts caused by timing constraint violations.
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