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TITLE: Exact Simulation of the Equilibrium Distribution of
Reflected Stochastic
Networks with Levy Input
SPEAKER: Jose Blanchet
ABSTRACT:
Reflected stochastic networks arise in the analysis of a large class
of
queueing systems. The most popular model of this type is perhaps
reflected Brownian motion, which arises in the heavy-traffic
analysis of
generalized Jackson networks. In this talk we discuss Monte Carlo
simulation strategies for the steady-state analysis of reflected
stochastic networks. In particular, we show how to exactly (i.e.
without
bias) simulate the equilibrium distribution of a reflected
stochastic
network with compound Poisson input and how to provide samples that
are
close (with explicit and controlled error bounds) to both the
transient
and the steady-state distribution of reflected Brownian motion in
the
positive orthant. (Joint work with Xinyun Chen.)
