The SURE approach to reliability analysis
The SURE computer program, a reliability-analysis tool for ultrareliable computer-system architectures, provides an efficient means for computing reasonably accurate upper and lower bounds for the death state probabilities of a large class of semi-Markov models. Once a semi-Markov model is described...
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Published in | IEEE transactions on reliability Vol. 41; no. 2; pp. 210 - 218 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Legacy CDMS
IEEE
01.06.1992
Institute of Electrical and Electronics Engineers |
Subjects | |
Online Access | Get full text |
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Summary: | The SURE computer program, a reliability-analysis tool for ultrareliable computer-system architectures, provides an efficient means for computing reasonably accurate upper and lower bounds for the death state probabilities of a large class of semi-Markov models. Once a semi-Markov model is described using a simple input language, SURE automatically computes the upper and lower bounds on the probability of system failure. A parameter of the model can be specified as a variable over a range of values, thus directing SURE to perform a sensitivity analysis automatically. The program provides a rapid computational capability for semi-Markov models useful for describing the fault-handling behavior of fault-tolerant computer systems. The only modeling restriction imposed by the program is that the nonexponential recovery transitions must be fast in comparison to the mission time. The SURE reliability-analysis method uses a fast bounding theorem based on means and variances and yields upper and lower bounds on the probability of system failure. Techniques have been developed to enable SURE to solve models with loops and calculate the operational-state probabilities. The computation is extremely fast, and large state-spaces can be directly solved; a pruning technique enables SURE to process extremely large models.< > |
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Bibliography: | CDMS Legacy CDMS ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0018-9529 1558-1721 |
DOI: | 10.1109/24.257783 |