An Aggregation Technique for the Transient Analysis of Stiff Markov Chains
IEEE Transactions on Computers
Distributed Computing Network Reliability
Distributed Computing Network Reliability
Reliability Models for Fault-Tolerant Private Network Applications
IEEE Transactions on Computers
Performance-Related Reliability Measures for Computing Systems
IEEE Transactions on Computers
IEEE Transactions on Computers
A modular approach for model-based dependability evaluation of a class of systems
ISAS'04 Proceedings of the First international conference on Service Availability
Hi-index | 0.00 |
Fault trees and Markov chains are commonly used for dependability modeling. Markov chains are powerful in that various kinds of dependencies can be easily modeled that fault tree models have difficulty capturing, but the state space grows exponentially in the number of components. Fault tree models are adequate for computing the reliability of non-repairable systems, but a state space description becomes necessary for repairable systems due to induced dependencies (even when all failure and repair processes are otherwise independent). In this paper we demonstrate that a decomposition approach can be used to avoid a full-system Markov reliability model for repairable systems with independent failure and repair processes. For an $n$-component system, $n$ 3-state sub-models can replace a full-system monolithic model. This is an approximation because the parameters used in the sub-model are approximately derived from the monolithic model.