  We present an approach that can be useful when the network or system
performance is described by a model that is not Markovian. Although most
performance models are based on Markov chains or Markov processes, in some
cases the Markov property does not hold. This can occur, for example, when the
system exhibits long range dependencies. For such situations, and other
non-Markovian cases, our method may provide useful help.
