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Dividing the expected occupation time of a continuous-time Markov chain by $t$ and letting $t\to \infty$ givesFor $\lambda ,\mu >0$ this is also the almost-sure long-run time fraction, not just the limit of an expectation. The two-state continuous-time Markov chain is irreducible and positive recurrent. Alternatively, a regeneration cycle consists of a mean $1/\lambda$ symptom-free holding time followed by a mean $1/\mu$ symptomatic holding time; the renewal-reward theorem gives $(1/\mu )/(1/\lambda +1/\mu )=\lambda /(\lambda +\mu )$. This agrees with the stationary distribution solving ${\pi }_1\lambda ={\pi }_2\mu$ and ${\pi }_1+{\pi }_2=1$.

Starting in state 1, if $\lambda =0$ the fraction is zero; if $\lambda >0$ and $\mu =0$ the patient eventually enters state 2 forever, and the fraction is one. If both rates vanish, the fraction remains zero from this initial state and the quotient $0/0$ is undefined.

A renewal-reward process consists of i.i.d. regenerative cycles $({\xi }_n,R_n)$, where ${\xi }_n>0$ is the $n$th cycle length and $R_n$ is the reward accumulated during that cycle. If $0<\mathbb{E}{\xi }_1<\infty$ and $\mathbb{E}|R_1|<\infty$, the renewal-reward theorem states that the cumulative reward $Z(t)$ satisfiesalmost surely, with the usual negligible-remainder condition for an unfinished cycle.

For the machine, let $X\sim \mathrm{Exp}(\lambda )$ be its working lifetime after a repair. The next repair occurs at the first inspection following failure, so one repair-to-repair cycle has lengthwhile its working-time reward is $R=X$. Since an exponential distribution of rate $\lambda$ has mean $1/\lambda$,By the tail-sum formula for the positive integer-valued variable $\lceil X/m\rceil$,Thereforeand the long-run proportion of time for which the machine is working is

For an M-M-1 queue, the first service lasts on average $1/\mu$. During it, the mean number of arrivals is $\lambda /\mu$, and each arrival initiates another statistically identical busy-period family. Thus, when $\lambda <\mu$,so the mean busy period of an M-M-1 queue isFor $\lambda =\mu$ the busy period is almost surely finite but has infinite mean, while for $\lambda >\mu$ it is infinite with positive probability; in either case its expectation is infinite.

For an M-M-infinity queue, stationary occupancy is Poisson with mean $\rho =\lambda /\mu$, so the long-run probability of an empty system isThe process alternates between idle periods and busy periods. An idle period waits for the next Poisson arrival, so its mean is $1/\lambda$. The renewal-reward theorem, with reward equal to time spent empty, givesSolving yields the mean busy period of an M-M-infinity queue: