Decay of tails at equilibrium for FIFO join the shortest queue networks
Abstract
In join the shortest queue networks, incoming jobs are assigned to the shortest queue from among a randomly chosen subset of queues, in a system of queues; after completion of service at its queue, a job leaves the network. We also assume that jobs arrive into the system according to a rate- Poisson process, , with rate-1 service at each queue. When the service at queues is exponentially distributed, it was shown in Vvedenskaya et al. [Probl. Inf. Transm. 32 (1996) 15-29] that the tail of the equilibrium queue size decays doubly exponentially in the limit as . This is a substantial improvement over the case D=1, where the queue size decays exponentially. The reasoning in [Probl. Inf. Transm. 32 (1996) 15-29] does not easily generalize to jobs with nonexponential service time distributions. A modularized program for treating general service time distributions was introduced in Bramson et al. [In Proc. ACM SIGMETRICS (2010) 275-286]. The program relies on an ansatz that asserts, in equilibrium, any fixed number of queues become independent of one another as . This ansatz was demonstrated in several settings in Bramson et al. [Queueing Syst. 71 (2012) 247-292], including for networks where the service discipline is FIFO and the service time distribution has a decreasing hazard rate. In this article, we investigate the limiting behavior, as , of the equilibrium at a queue when the service discipline is FIFO and the service time distribution has a power law with a given exponent , for . We show under the above ansatz that, as , the tail of the equilibrium queue size exhibits a wide range of behavior depending on the relationship between and . In particular, if , the tail is doubly exponential and, if , the tail has a power law. When , the tail is exponentially distributed.
Keywords
Cite
@article{arxiv.1106.4582,
title = {Decay of tails at equilibrium for FIFO join the shortest queue networks},
author = {Maury Bramson and Yi Lu and Balaji Prabhakar},
journal= {arXiv preprint arXiv:1106.4582},
year = {2013}
}
Comments
Published in at http://dx.doi.org/10.1214/12-AAP888 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)