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GI/GI/1 queues with infinite means of service time and interarrival time

Szczotka W.

Probability and Mathematical Statistics

2016

Vol. 36, Fasc. 2

279--293

The main results deal with the GI/GI/1 queues with Infinite means of the service times and interarrival times. Theorem 3.1 gives an asymptotic, in a heavy traffic situation, of the sequence of waiting times of the consecutive customers. Theorem 4.1 gives an asymptotic of stationary waiting times in a heavy traffic situation. In a special case, the asymptotic stationary waiting times have an exponential distribution (Corollary 4.1).

Tightness of stationary waiting times in heavy traffic for G1/G1/1 queues with thick tails

Czystołowski M., Szczotka W.

Probability and Mathematical Statistics

2007

Vol. 27, Fasc. 1

109--123

Recently, a Heavy Traffic Invariance Principle was proposed by Szczotka and Woyczyński to characterize the heavy traffie limiting distribution of normalized stationary waiting times of G/G/X queues in terms of an appropriate convergence to a Levy process. It has two important assumptions. The first of them deals with a convergence to a Levy process of appropriate processes which is well investigated in the literature. The second one states that the sequence of appropriate normalized stationary waiting times is tight. In the present paper we characterize the tightness condition for the case of GI/GI/1 queues in terms of the first condition.

Heavy-tailed dependent queues in heavy traffic

Szczotka W., Woyczyński W. A.

Probability and Mathematical Statistics

2004

Vol. 24, Fasc. 1

67--96

The paper studies G/G/1 queues with heavy-tailed probability distributions of the service times and/or the interarrival times. It relies on the fact that the heavy traffic limiting distribution of the normalized stationary waiting times for such queues is equal to the distribution of the supremum M = sup0 ≤ t < ∞ (X(t)−βt), where X is a Lévy process. This distribution turns out to be exponential if the tail of the distribution of interarrival times is heavier than that of the service times, and it has a more complicated non-exponential shape in the opposite case; if the service times have heavy-tailed distribution in the domain of attraction of a one-sided α-stable distribution, then the limit distribution is Mittag-Leffler’s. In the case of a symmetric α-stable process X, the Laplace transform of the distribution of the supremum M is also given. Taking into account the known relationship between the heavy-traffic-regime distribution of queue length and its waiting time, asymptotic results for the former are also provided. Statistical dependence between the sequence of service times and the sequence of interarrival times, as well as between random variables within each of these two sequences, is allowed. Several examples are provided.