Fault-Tolerant Multiprocessor Systems Reliability Estimation Using Statistical Experiments with GL-Models

The article is focused on the reliability estimation of fault-tolerant multiprocessor systems with huge number of processors and complex behavior of the systems on its processors failures. A universal method for the reliability estimation of such fault-tolerant multi-processor systems for a given time period is proposed. The method is based on conducting statistical experiments (Monte-Carlo) with models that adequately reflect the behavior of the fault-tolerant multiprocessor systems in the flow of failures. For that purpose, it is suggested using GL-model, which is a graph with special way formed Boolean functions assigned to its edges. The questions of synthesis, minimization and transformation of such models are considered. The article addresses the statistical estimation error (Monte-Carlo Error). The upper bound for calculating the error before conducting statistical experiments is suggested. It is shown that the error could be estimated more precisely using the results of conducted statistical experiments. Correspondent statistical estimator is proposed.