Stationary Distribution Convergence for Generalized Jackson Networks in Heavy Traffic
Author(s) -
Amarjit Budhiraja,
Chihoon Lee
Publication year - 2009
Publication title -
mathematics of operations research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.619
H-Index - 83
eISSN - 1526-5471
pISSN - 0364-765X
DOI - 10.1287/moor.1080.0353
Subject(s) - mathematics , convergence (economics) , stationary distribution , limit (mathematics) , brownian motion , queueing theory , queue , reflected brownian motion , heavy traffic approximation , independence (probability theory) , stability (learning theory) , weak convergence , distribution (mathematics) , statistical physics , mathematical analysis , geometric brownian motion , computer science , diffusion process , discrete mathematics , markov chain , statistics , knowledge management , physics , innovation diffusion , machine learning , economics , programming language , economic growth
In a recent paper, Gamarnik and Zeevi [Gamarnik, D., A. Zeevi. 2006. Validity of heavy traffic steady-state approximations in open queueing networks. Ann. Appl. Probab.16(1) 56--90], it was shown that under suitable conditions stationary distributions of the (scaled) queue-lengths process for a generalized Jackson network converge to the stationary distribution of the associated reflected Brownian motion in the heavy traffic limit. The proof relied on certain exponential integrability assumptions on the primitives of the network. In this note we show that the above result holds under much weaker integrability conditions. We provide an alternative proof of this result assuming (in addition to natural heavy traffic and stability assumptions) only standard independence and square integrability conditions on the network primitives that are commonly used in heavy traffic analysis. Furthermore, under additional integrability conditions we establish convergence of moments of stationary distributions.
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