Analysis of RealTime Embedded Applications in the Presence of a Stochastic Fault Model

Real-time embedded systems are characterized by their need to complete task executions within their scheduled deadlines. Task scheduling could get complicated due to the occurrence of faults in such systems. Traditionally, researchers have accounted for faults by assuming a constant number of faults occuring in the system. However, such an assumption may lead the system designers to rather pessimistic estimates on tasks´ response times. In this paper, the occurrence of faults has been modeled as a stochastic process with a Poisson distribution having a mean inter-arrival rate of lambda. The usefulness of the model has been evaluated in terms of estimated task response time, energy consumption and loss of predictability for two periodic real-time embedded task sets, one real-world and another synthetic. It has been shown that the proposed approach can achieve up to 40% improvement in terms of the estimated energy savings and task response time with very minimal loss of predictability in the system of about 1e-3 % as compared to the systems with constant number faults