1,994 research outputs found
Proportional fairness and its relationship with multi-class queueing networks
We consider multi-class single-server queueing networks that have a product
form stationary distribution. A new limit result proves a sequence of such
networks converges weakly to a stochastic flow level model. The stochastic flow
level model found is insensitive. A large deviation principle for the
stationary distribution of these multi-class queueing networks is also found.
Its rate function has a dual form that coincides with proportional fairness. We
then give the first rigorous proof that the stationary throughput of a
multi-class single-server queueing network converges to a proportionally fair
allocation. This work combines classical queueing networks with more recent
work on stochastic flow level models and proportional fairness. One could view
these seemingly different models as the same system described at different
levels of granularity: a microscopic, queueing level description; a
macroscopic, flow level description and a teleological, optimization
description.Comment: Published in at http://dx.doi.org/10.1214/09-AAP612 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Closed queueing networks under congestion: non-bottleneck independence and bottleneck convergence
We analyze the behavior of closed product-form queueing networks when the
number of customers grows to infinity and remains proportionate on each route
(or class). First, we focus on the stationary behavior and prove the conjecture
that the stationary distribution at non-bottleneck queues converges weakly to
the stationary distribution of an ergodic, open product-form queueing network.
This open network is obtained by replacing bottleneck queues with per-route
Poissonian sources whose rates are determined by the solution of a strictly
concave optimization problem. Then, we focus on the transient behavior of the
network and use fluid limits to prove that the amount of fluid, or customers,
on each route eventually concentrates on the bottleneck queues only, and that
the long-term proportions of fluid in each route and in each queue solve the
dual of the concave optimization problem that determines the throughputs of the
previous open network.Comment: 22 page
Store-Forward and its implications for Proportional Scheduling
The Proportional Scheduler was recently proposed as a scheduling algorithm
for multi-hop switch networks. For these networks, the BackPressure scheduler
is the classical benchmark. For networks with fixed routing, the Proportional
Scheduler is maximum stable, myopic and, furthermore, will alleviate certain
scaling issued found in BackPressure for large networks. Nonetheless, the
equilibrium and delay properties of the Proportional Scheduler has not been
fully characterized.
In this article, we postulate on the equilibrium behaviour of the
Proportional Scheduler though the analysis of an analogous rule called the
Store-Forward allocation. It has been shown that Store-Forward has
asymptotically allocates according to the Proportional Scheduler. Further, for
Store-Forward networks, numerous equilibrium quantities are explicitly
calculable. For FIFO networks under Store-Forward, we calculate the policies
stationary distribution and end-to-end route delay. We discuss network
topologies when the stationary distribution is product-form, a phenomenon which
we call \emph{product form resource pooling}. We extend this product form
notion to independent set scheduling on perfect graphs, where we show that
non-neighbouring queues are statistically independent. Finally, we analyse the
large deviations behaviour of the equilibrium distribution of Store-Forward
networks in order to construct Lyapunov functions for FIFO switch networks
Monotonicity and error bounds for networks of Erlang loss queues
Networks of Erlang loss queues naturally arise when modelling finite communication systems without delays, among which, most notably are (i) classical circuit switch telephone networks (loss networks) and (ii) present-day wireless mobile networks. Performance measures of interest such as loss probabilities or throughputs can be obtained from the steady state distribution. However, while this steady state distribution has a closed product form expression in the first case (loss networks), it does not have one in the second case due to blocked (and lost) handovers. Product form approximations are therefore suggested. These approximations are obtained by a combined modification of both the state space (by a hypercubic expansion) and the transition rates (by extra redial rates). It will be shown that these product form approximations lead to (1) upper bounds for loss probabilities and \ud
(2) analytic error bounds for the accuracy of the approximation for various performance measures.\ud
The proofs of these results rely upon both monotonicity results and an analytic error bound method as based on Markov reward theory. This combination and its technicalities are of interest by themselves. The technical conditions are worked out and verified for two specific applications:\ud
(1)• pure loss networks as under (2)• GSM networks with fixed channel allocation as under.\ud
The results are of practical interest for computational simplifications and, particularly, to guarantee that blocking probabilities do not exceed a given threshold such as for network dimensioning
Towards a System Theoretic Approach to Wireless Network Capacity in Finite Time and Space
In asymptotic regimes, both in time and space (network size), the derivation
of network capacity results is grossly simplified by brushing aside queueing
behavior in non-Jackson networks. This simplifying double-limit model, however,
lends itself to conservative numerical results in finite regimes. To properly
account for queueing behavior beyond a simple calculus based on average rates,
we advocate a system theoretic methodology for the capacity problem in finite
time and space regimes. This methodology also accounts for spatial correlations
arising in networks with CSMA/CA scheduling and it delivers rigorous
closed-form capacity results in terms of probability distributions. Unlike
numerous existing asymptotic results, subject to anecdotal practical concerns,
our transient one can be used in practical settings: for example, to compute
the time scales at which multi-hop routing is more advantageous than single-hop
routing
Monotonicity and error bounds for networks of Erlang loss queues
Networks of Erlang loss queues naturally arise when modelling finite communication systems without delays, among which, most notably\ud
(i) classical circuit switch telephone networks (loss networks) and\ud
(ii) present-day wireless mobile networks.\ud
\ud
Performance measures of interest such as loss probabilities or throughputs can be obtained from the steady state distribution. However, while this steady state distribution has a closed product form expression in the first case (loss networks), it has not in the second case due to blocked (and lost) handovers. Product form approximations are therefore suggested. These approximations are obtained by a combined modification of both the state space (by a hyper cubic expansion) and the transition rates (by extra redial rates). It will be shown that these product form approximations lead to\ud
\ud
- secure upper bounds for loss probabilities and\ud
- analytic error bounds for the accuracy of the approximation for various performance measures.\ud
\ud
The proofs of these results rely upon both monotonicity results and an analytic error bound method as based on Markov reward theory. This combination and its technicalities are of interest by themselves. The technical conditions are worked out and verified for two specific applications:\ud
\ud
- pure loss networks as under (i)\ud
- GSM-networks with fixed channel allocation as under (ii).\ud
\ud
The results are of practical interest for computational simplifications and, particularly, to guarantee blocking probabilities not to exceed a given threshold such as for network dimensioning.\u
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