Fault trees and Markov models for reliability analysis of fault-tolerant digital systems

Abstract

Reliability analysis of fault tolerant computer systems for critical applications is complicated by several factors. In this paper, we discuss these modeling difficulties and describe and demonstrate approaches to handling them. Three important techniques characterize our approach. First, behavioral decomposition separates the system failure modes specification from the recovery process specification. Second, a fault tree representation of the system failure modes is converted to an equivalent Markov model, to which the recovery models are added automatically. Third, the fault tree to Markov chain conversion allows the definition of new dynamic fault tree gates to capture the sequence dependent failure modes that are often associated with advanced fault tolerant systems. Two advanced fault tolerant computer systems are described, and fault tree models for their analysis are presented. HARP (the Hybrid Automated Reliability Predictor) is a software package developed at Duke University and NASA Langley Research Center that is used to analyze the example systems.