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August 2005 Instability in stochastic and fluid queueing networks
David Gamarnik, John J. Hasenbein
Ann. Appl. Probab. 15(3): 1652-1690 (August 2005). DOI: 10.1214/105051605000000179


The fluid model has proven to be one of the most effective tools for the analysis of stochastic queueing networks, specifically for the analysis of stability. It is known that stability of a fluid model implies positive (Harris) recurrence (stability) of a corresponding stochastic queueing network, and weak stability implies rate stability of a corresponding stochastic network. These results have been established both for cases of specific scheduling policies and for the class of all nonidling policies.

However, only partial converse results have been established and in certain cases converse statements do not hold. In this paper we close one of the existing gaps. For the case of networks with two stations, we prove that if the fluid model is not weakly stable under the class of all nonidling policies, then a corresponding queueing network is not rate stable under the class of all nonidling policies. We establish the result by building a particular nonidling scheduling policy which makes the associated stochastic process transient. An important corollary of our result is that the condition ρ*≤1, which was proven in [Oper. Res. 48 (2000) 721–744] to be the exact condition for global weak stability of the fluid model, is also the exact global rate stability condition for an associated queueing network. Here ρ* is a certain computable parameter of the network involving virtual station and push start conditions.


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David Gamarnik. John J. Hasenbein. "Instability in stochastic and fluid queueing networks." Ann. Appl. Probab. 15 (3) 1652 - 1690, August 2005.


Published: August 2005
First available in Project Euclid: 15 July 2005

zbMATH: 1135.90006
MathSciNet: MR2152240
Digital Object Identifier: 10.1214/105051605000000179

Primary: 90B15
Secondary: 60F10 , 60K25

Keywords: fluid models , large deviations , Rate stability

Rights: Copyright © 2005 Institute of Mathematical Statistics


Vol.15 • No. 3 • August 2005
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