The reliability of life-critical computer systems

In order to aid the designers of life-critical, fault-tolerant computing systems, accurate and efficient methods for reliability prediction are needed. The accuracy requirement implies the need to model the system in great detail, and hence the need to address the problems of large state space, non-exponential distributions, and error analysis. The efficiency requirement implies the need for new model solution techniques, in particular the use of decomposition/aggregation in the context of a hybrid model. We describe a model for reliability prediction which meets both requirements. Specifically, our model is partitioned into fault occurrence and fault/error handling submodels, which are represented by non-homogeneous Markov processes and extended stochastic Petri nets, respectively. The overall aggregated model is a stochastic process that is solved by numerical techniques. Methods to analyze the effects of variations in input parameters on the resulting reliability predictions are also provided. © 1986 Springer-Verlag.

Duke Scholars

Author Kishor S. Trivedi Electrical and Computer Engineering