The classical random walk of which the one-step displacement variable u has a first finite negative moment is considered. The R.W. possesses a unique stationary distribution; x is a random variable with this distribution. It is assumed that the right-hand and/or the left-hand tail of the distribution of u are heavy-tailed. For the type of heavy-tailed distribution considered in this study a contraction factor &Dgr;(a) exists with &Dgr;(a)↓0 for a↑1, and a ↑1 is equivalent with E{u}↑0. It is shown that &Dgr;(a)x converges in distribution for a↑1. It is the analysis of the tail of this limiting distribution of &Dgr;(a)x which is the main distribution for a↑1. It is the analysis of the tail of this limiting distribution of &Dgr;(a)x which is the main purpose of the present study in particular when u is a mix of stochastic variables ui, i=1,…, N, each ui having its own heavy tail characteristics for its right- and left-hand tails. For an important case it is shown that for the tail of the distribution of &Dgr;(a)x an symptotic expression in the variables &Dgr;(a) and t for &Dgr;(a)↓0 and t→∞ can be derived. For this asymptotic relation the dominating term is completely determined by the heavier tail of the 2N tails of the ui; the other terms of the asymptotic relation show the influence of less heavy tails and, depending on t, the terms may have a contribution which is not always negligible. The study starts with the derivation of a functional equation for the L.S.-transform of the distribution of x and that of the excess distribution of the stationary idle time distribution. For several important cases this functional equation could be solved and thus has led to the above mentioned asymptotic result. The derivation of it required quite some preparation, because it needed an effective description of the heavy-tailed jump sequence u. It was obtained by prescribing the heavy-tailed distributions of [u]+=max (0,u) and [u]–=min (0,u). It is shown that the random walk can serve as a model for the actual waiting process of a GI/G/1 queueing model; in that case the distribution of u is that of the difference of the service time and the interarrival time. The analysis of the present study then describes the heavy-traffic theory for the case with heavy-tailed service- and/or interarrival time distribution.
