Abstract
A new method to bound the steady-state solution of large Markov chains is presented. The method integrates the concepts of eigenvector polyhedron and of aggregation. It is specially suited for Markov chains with high locality and very large state spaces.
A model of a repairable fault-tolerant system with 16 millions states is used as an example. Bounds on its availability are obtained by considering a small part of its state space only. The method is potentially useful to bound other types of dependability requirements.
This paper is in part based on [19].
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© 1995 ECSC — EC — EAEC, Brussels — Luxembourg
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Courtois, PJ., Semal, P. (1995). Computable Dependability Bounds for Large Markov Chains. In: Randell, B., Laprie, JC., Kopetz, H., Littlewood, B. (eds) Predictably Dependable Computing Systems. ESPRIT Basic Research Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79789-7_28
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DOI: https://doi.org/10.1007/978-3-642-79789-7_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-79791-0
Online ISBN: 978-3-642-79789-7
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