A new general-purpose method for the computation of the interval availability distribution

Abstract

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.