We analyse the tail behaviour of stationary response times in the class of
open stochastic networks with renewal input admitting a representation as (
max,+)-linear systems. For a K-station tandem network of single server queu
es with infinite buffer capacity, which is one of the simplest models in th
is class, we first show that if the tail of the service time distribution o
f one server, say server i(0) is an element of {1,...,K}, is subexponential
and heavier than those of the other servers, then the stationary distribut
ion of the response time until the completion of service at server j greate
r than or equal to i(0) asymptotically behaves like the stationary response
time distribution in an isolated single-server queue with server i(0). Sim
ilar asymptotics are given in the case when several service time distributi
ons are subexponential and asymptotically tail-equivalent. This result is t
hen extended to the asymptotics of general (max,+)-linear systems associate
d with i.i.d. driving matrices having one (or more) dominant diagonal entry
in the subexponential class. In the irreducible case, the asymptotics are
surprisingly simple, in comparison with results of the same kind in the Cra
mer case: the asymptotics only involve the excess distribution of the domin
ant diagonal entry, the mean value of this entry, the intensity of the arri
val process, and the Lyapunov exponent of the sequence of driving matrices.
In the reducible case, asymptotics of the same kind, though somewhat more
complex, are also obtained. As a direct application, we give the asymptotic
s of stationary response times in a class of stochastic Petri nets called e
vent graphs. This is based on the assumption that the firing times are inde
pendent and that the tail of the firing times of one of the transitions is
subexponential and heavier than those of the others. An extension of these
results to nonrenewal input processes is discussed. Asymptotics of queue si
ze processes are also considered.