Real-time embedded systems are characterized by their need to complete task executions within their scheduled dead- lines. Task scheduling could get complicated due to the oc- currence 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 assump- tion may lead the system designers to rather pessimistic es- timates on tasks? response times. In this paper, the occur- rence of faults has been modeled as a stochastic process with a Poisson distribution having a mean inter-arrival rate of. 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.