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The reliability of life-critical computer systems

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Summary

In order to aid the designers of life-critical, fault-tolerant computing systems, accurate and efficient methods for reliability prediction are needed. The accuracy requirement implies the need to model the system in great detail, and hence the need to address the problems of large state space, non-exponential distributions, and error analysis. The efficiency requirement implies the need for new model solution techniques, in particular the use of decomposition/aggregation in the context of a hybrid model. We describe a model for reliability prediction which meets both requirements. Specifically, our model is partitioned into fault occurrence and fault/error handling submodels, which are represented by non-homogeneous Markov processes and extended stochastic Petri nets, respectively. The overall aggregated model is a stochastic process that is solved by numerical techniques. Methods to analyze the effects of variations in input parameters on the resulting reliability predictions are also provided.

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Authors and Affiliations

  1. Department of Computer Science, Clemson University, 29631, Clemson, SC, USA

    Robert Geist & Mark Smotherman

  2. Department of Computer Science, Duke University, 27706, Durham, NC, USA

    Kishor Trivedi & Joanne Bechta Dugan

Authors
  1. Robert Geist
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  2. Mark Smotherman
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  3. Kishor Trivedi
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  4. Joanne Bechta Dugan
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Additional information

This work was supported in part by NASA grant NAG 1-70 and by an equipment grant from the Concurrent Computer Corp

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Geist, R., Smotherman, M., Trivedi, K. et al. The reliability of life-critical computer systems. Acta Informatica 23, 621–642 (1986). https://doi.org/10.1007/BF00264310

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