Open Access
August 1999 Fast Jackson networks
J. B. Martin, Yu. M. Suhov
Ann. Appl. Probab. 9(3): 854-870 (August 1999). DOI: 10.1214/aoap/1029962816

Abstract

We extend the results of Vvedenskaya, Dobrushin and Karpelevich to Jackson networks. Each node j,1jJ of the network consists of N identical channels, each with an infinite buffer and a single server with service rate μj. The network is fed by a family of independent Poisson flows of rates Nλ1,,NλJ arriving at the corresponding nodes. After being served at node j, a task jumps to node k with probability pjk and leaves the network with probability pj=1Σkpjk. Upon arrival at any node, a task selects m of the N channels there at random and joins the one with the shortest queue. The state of the network at time t0 may be described by the vector r(t)=rj(n,t),1jJ,nϵZ+, where rj(n,t) is the proportion of channels at node j with queue length at least n at time t. We analyze the limit N. We show that, under a standard nonoverload condition, the limiting invariant distribution (ID) of the process r is concentrated at a single point, and the process itself asymptotically approaches a single trajectory. This trajectory is identified with the solution to a countably infinite system of ODE's, whose fixed point corresponds to the limiting ID. Under the limiting ID, the tail of the distribution of queue-lengths decays superexponentially, rather than exponentially as in the case of standard Jackson networks--hence the term "fast networks" in the title of the paper.

Citation

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J. B. Martin. Yu. M. Suhov. "Fast Jackson networks." Ann. Appl. Probab. 9 (3) 854 - 870, August 1999. https://doi.org/10.1214/aoap/1029962816

Information

Published: August 1999
First available in Project Euclid: 21 August 2002

zbMATH: 0972.90008
MathSciNet: MR1722285
Digital Object Identifier: 10.1214/aoap/1029962816

Subjects:
Primary: 90B15
Secondary: 60J27 , 60K25

Keywords: dynamic routing , Jackson network , Markov process , Queueing network , superexponential decay

Rights: Copyright © 1999 Institute of Mathematical Statistics

Vol.9 • No. 3 • August 1999
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