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Chaoticity on path space for a queueing network with selection of the shortest queue among several

Published online by Cambridge University Press:  14 July 2016

Carl Graham*
Affiliation:
École Polytechnique, Palaiseau
*
Postal address: CMAP, École Polytechnique, 91128 Palaiseau, France (UMR CNRS 7641). Email address: [email protected]

Abstract

We consider a network with N infinite-buffer queues with service rates λ, and global task arrival rate Nν. Each task is allocated L queues among N with uniform probability and joins the least loaded one, ties being resolved uniformly. We prove Q-chaoticity on path space for chaotic initial conditions and in equilibrium: any fixed finite subnetwork behaves in the limit N goes to infinity as an i.i.d. system of queues of law Q. The law Q is characterized as the unique solution for a non-linear martingale problem; if the initial conditions are q-chaotic, then Q0 = q, and in equilibrium Q0 = qρ is the globally attractive stable point of the Kolmogorov equation corresponding to the martingale problem. This result is equivalent to a law of large numbers on path space with limit Q, and implies a functional law of large numbers with limit (Qt)t≥0. The significant improvement in buffer utilization, due to the resource pooling coming from the choices, is precisely quantified at the limit.

Type
Research Papers
Copyright
Copyright © 2000 by The Applied Probability Trust 

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References

Ethier, S., and Kurtz, T. (1986). Markov Processes. John Wiley, New York.CrossRefGoogle Scholar
Graham, C. and Méléard, S. (1993). Propagation of chaos for a fully connected loss network with alternate routing. Stoch. Proc. Appl. 44, 159180.Google Scholar
Graham, C. and Méléard, S. (1994). Chaos hypothesis for a system interacting through shared resources. Prob. Theory Rel. Fields 100, 157173.CrossRefGoogle Scholar
Graham, C. and Méléard, S. (1997). Stochastic particle approximations for generalized Boltzmann models and convergence estimates. Ann. Prob. 28, 115132.Google Scholar
Martin, J. B. and Suhov, Yu. M. (1999). Fast Jackson networks. Ann. Appl. Prob. 9, 854870.Google Scholar
Shiga, T., and Tanaka, H. (1985). Central limit theorem for a system of Markovian particles with mean field interaction. Z. Wahrscheinlichkeitsth. 69, 439459.CrossRefGoogle Scholar
Stoyan, D. (1983). Comparison Methods for Queues and other Stochastic Models, ed. Daley, D. J. John Wiley, New York.Google Scholar
Sznitman, A. S. (1991). Propagation of chaos. In École d'été de probabilites de Saint-Flour XIX – 1989 (Lecture Notes in Math. 1464). Springer, Berlin, pp. 165251.Google Scholar
Turner, S. (1998). The effect of increasing routing choice on resource pooling. Prob. Eng. Inf. Sci. 12, 109124.Google Scholar
Whitt, W. (1985). Blocking when service is required from several facilities simultaneously. AT&T Tech. J. 64, 18071856.Google Scholar
Vvedenskaya, N. and Suhov, Yu. M. (1997). Dobrushin's mean-field approximation for a queue with dynamic routing. Markov Proc. Rel. Fields 3, 493526.Google Scholar
Vvedenskaya, N., Dobrushin, R., and Karpelevich, F. (1996). Queueing system with selection of the shortest of two queues: an asymptotic approach. Prob. Inf. Trans. 32, 1527.Google Scholar