1,228 research outputs found
A sufficient condition for the subexponential asymptotics of GI/G/1-type Markov chains with queueing applications
The main contribution of this paper is to present a new sufficient condition
for the subexponential asymptotics of the stationary distribution of a
GI/GI/1-type Markov chain without jumps from level "infinity" to level zero.
For simplicity, we call such Markov chains {\it GI/GI/1-type Markov chains
without disasters} because they are often used to analyze semi-Markovian queues
without "disasters", which are negative customers who remove all the customers
in the system (including themselves) on their arrivals. In this paper, we
demonstrate the application of our main result to the stationary queue length
distribution in the standard BMAP/GI/1 queue. Thus we obtain new asymptotic
formulas and prove the existing formulas under weaker conditions than those in
the literature. In addition, applying our main result to a single-server queue
with Markovian arrivals and the -bulk-service rule (i.e., MAP//1 queue), we obatin a subexponential asymptotic formula for the
stationary queue length distribution.Comment: Submitted for revie
Exact asymptotics for fluid queues fed by multiple heavy-tailed on-off flows
We consider a fluid queue fed by multiple On-Off flows with heavy-tailed
(regularly varying) On periods. Under fairly mild assumptions, we prove that
the workload distribution is asymptotically equivalent to that in a reduced
system. The reduced system consists of a ``dominant'' subset of the flows, with
the original service rate subtracted by the mean rate of the other flows. We
describe how a dominant set may be determined from a simple knapsack
formulation. The dominant set consists of a ``minimally critical'' set of
On-Off flows with regularly varying On periods. In case the dominant set
contains just a single On-Off flow, the exact asymptotics for the reduced
system follow from known results. For the case of several
On-Off flows, we exploit a powerful intuitive argument to obtain the exact
asymptotics. Combined with the reduced-load equivalence, the results for the
reduced system provide a characterization of the tail of the workload
distribution for a wide range of traffic scenarios
Validity of heavy traffic steady-state approximations in generalized Jackson Networks
We consider a single class open queueing network, also known as a generalized
Jackson network (GJN). A classical result in heavy-traffic theory asserts that
the sequence of normalized queue length processes of the GJN converge weakly to
a reflected Brownian motion (RBM) in the orthant, as the traffic intensity
approaches unity. However, barring simple instances, it is still not known
whether the stationary distribution of RBM provides a valid approximation for
the steady-state of the original network. In this paper we resolve this open
problem by proving that the re-scaled stationary distribution of the GJN
converges to the stationary distribution of the RBM, thus validating a
so-called ``interchange-of-limits'' for this class of networks. Our method of
proof involves a combination of Lyapunov function techniques, strong
approximations and tail probability bounds that yield tightness of the sequence
of stationary distributions of the GJN.Comment: Published at http://dx.doi.org/10.1214/105051605000000638 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
Perturbation Analysis of a Variable M/M/1 Queue: A Probabilistic Approach
Motivated by the problem of the coexistence on transmission links of
telecommunication networks of elastic and unresponsive traffic, we study in
this paper the impact on the busy period of an M/M/1 queue of a small
perturbation in the server rate. The perturbation depends upon an independent
stationary process (X(t)) and is quantified by means of a parameter \eps \ll 1.
We specifically compute the two first terms of the power series expansion in
\eps of the mean value of the busy period duration. This allows us to study the
validity of the Reduced Service Rate (RSR) approximation, which consists in
comparing the perturbed M/M/1 queue with the M/M/1 queue where the service rate
is constant and equal to the mean value of the perturbation. For the first term
of the expansion, the two systems are equivalent. For the second term, the
situation is more complex and it is shown that the correlations of the
environment process (X(t)) play a key role
Estimating customer impatience in a service system with unobserved balking
This paper studies a service system in which arriving customers are provided
with information about the delay they will experience. Based on this
information they decide to wait for service or to leave the system. The main
objective is to estimate the customers' patience-level distribution and the
corresponding potential arrival rate, using knowledge of the actual
queue-length process only. The main complication, and distinguishing feature of
our setup, lies in the fact that customers who decide not to join are not
observed, but, remarkably, we manage to devise a procedure to estimate the load
they would generate. We express our system in terms of a multi-server queue
with a Poisson stream of customers, which allows us to evaluate the
corresponding likelihood function. Estimating the unknown parameters relying on
a maximum likelihood procedure, we prove strong consistency and derive the
asymptotic distribution of the estimation error. Several applications and
extensions of the method are discussed. The performance of our approach is
further assessed through a series of numerical experiments. By fitting
parameters of hyperexponential and generalized-hyperexponential distributions
our method provides a robust estimation framework for any continuous
patience-level distribution
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