Decomposition in Reliability Analysis of Fault-Tolerant Systems

Abstract

Two important problems which arise in modeling fault-tolerant systems with ultra-high reliability requirements are discussed. 1) Any analytic model of such a system has a large number of states, making the solution computationally intractable. This leads to the need for decomposition techniques. 2) The common assumption of exponential holding times in the states is intolerable while modeling such systems. Approaches to solving this problem are reviewed. A major notion described in the attempt to deal with reliability models with a large number of states is that of behavioral decomposition followed by aggregation. Models of the fault-handling processes are either semi-Markov or simulative in nature, thus removing the usual restrictions of exponential holding times within the coverage model. The aggregate fault-occurrence model is a non-homogeneous Markov chain, thus allowing the times to failure to possess Weibull-like distributions. There are several potential sources of error in this approach to reliability modeling. The decomposition/aggregation process involves the error in estimating the transition parameters. The numerical integration involves discretization and round-off errors. Analysis of these errors and questions of sensitivity of the output (R(t)) to the inputs (failure rates and recovery model parameters) and to the initial system state acquire extreme importance when dealing with ultra-high reliability requirements.