We develop a new randomization-based general-purpose method for the computation of the interval availability
distribution of systems modeled by continuous-time Markov chains (CTMCs). The basic idea of
the new method is the use of a randomization construct with different randomization rates for up and down
states. The new method is numerically stable and computes the measure with well-controlled truncation error.
In addition, for large CTMC models, when the maximum output rates from up and down states are significantly
different, and when the interval availability has to be guaranteed to have a level close to one, the new
method is significantly or moderately less costly in terms of CPU time than a previous randomization-based
state-of-the-art method, depending on whether the maximum output rate from down states is larger than the
maximum output rate from up states, or vice versa. Otherwise, the new method can be more costly, but a relatively
inexpensive for large models switch of reasonable quality can be easily developed to choose the fastest
method. Along the way, we show the correctness of a generalized randomization construct, in which arbitrarily
different randomization rates can be associated with different states, for both finite CTMCs with infinitesimal
generator and uniformizable CTMCs with denumerable state space.
CitationCarrasco, J. A new general-purpose method for the computation of the interval availability distribution. "Informs journal on computing", 18 Novembre 2013, vol. 25, núm. 4, p. 774-791.
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