Sampling at subexponential times, with queueing applications

S. Asmussen, C. Klüppelberg and K. Sigman

Department of Mathematical Statistics,
Lund Institute of Technology,
Lund University,
1998

ISSN 0281-1944
ISRN LUNFD6/NFMS--3190--SE

Abstract:

We study the tail asymptotics of the r.v. X(T) where {X(t)} is a stochastic process with a linear drift and satisfying some regularity conditions like a central limit theorem and a large deviations principle, and T is an independent r.v. with a subexponential distribution. We find that the tail of X(T) is sensitive to whether or not T has a heavier or lighter tail than a Weibull distribution with tail e^-sqrt(x) . This leads to two distinct cases, heavy-tailed and moderately heavy-tailed, but also some results for the classical light-tailed case are given. The results are applied via distributional Little's law to establish tail asymptotics for steady-state queue length in GI/GI/1 queues with subexponential service times. Further applications are given for queues with vacations, and M/G/1 busy periods.

Key words:

busy period, independent sampling, Laplace's method, large deviations, Little's law, Markov additive process, Poisson process, random walk, regular variation, subexponential distribution, vacation model, Weibull distribution