50,389 research outputs found
Asymptotic optimality of maximum pressure policies in stochastic processing networks
We consider a class of stochastic processing networks. Assume that the
networks satisfy a complete resource pooling condition. We prove that each
maximum pressure policy asymptotically minimizes the workload process in a
stochastic processing network in heavy traffic. We also show that, under each
quadratic holding cost structure, there is a maximum pressure policy that
asymptotically minimizes the holding cost. A key to the optimality proofs is to
prove a state space collapse result and a heavy traffic limit theorem for the
network processes under a maximum pressure policy. We extend a framework of
Bramson [Queueing Systems Theory Appl. 30 (1998) 89--148] and Williams
[Queueing Systems Theory Appl. 30 (1998b) 5--25] from the multiclass queueing
network setting to the stochastic processing network setting to prove the state
space collapse result and the heavy traffic limit theorem. The extension can be
adapted to other studies of stochastic processing networks.Comment: Published in at http://dx.doi.org/10.1214/08-AAP522 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Many-server queues with customer abandonment: numerical analysis of their diffusion models
We use multidimensional diffusion processes to approximate the dynamics of a
queue served by many parallel servers. The queue is served in the
first-in-first-out (FIFO) order and the customers waiting in queue may abandon
the system without service. Two diffusion models are proposed in this paper.
They differ in how the patience time distribution is built into them. The first
diffusion model uses the patience time density at zero and the second one uses
the entire patience time distribution. To analyze these diffusion models, we
develop a numerical algorithm for computing the stationary distribution of such
a diffusion process. A crucial part of the algorithm is to choose an
appropriate reference density. Using a conjecture on the tail behavior of a
limit queue length process, we propose a systematic approach to constructing a
reference density. With the proposed reference density, the algorithm is shown
to converge quickly in numerical experiments. These experiments also show that
the diffusion models are good approximations for many-server queues, sometimes
for queues with as few as twenty servers
The supervised hierarchical Dirichlet process
We propose the supervised hierarchical Dirichlet process (sHDP), a
nonparametric generative model for the joint distribution of a group of
observations and a response variable directly associated with that whole group.
We compare the sHDP with another leading method for regression on grouped data,
the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method
on two real-world classification problems and two real-world regression
problems. Bayesian nonparametric regression models based on the Dirichlet
process, such as the Dirichlet process-generalised linear models (DP-GLM) have
previously been explored; these models allow flexibility in modelling nonlinear
relationships. However, until now, Hierarchical Dirichlet Process (HDP)
mixtures have not seen significant use in supervised problems with grouped data
since a straightforward application of the HDP on the grouped data results in
learnt clusters that are not predictive of the responses. The sHDP solves this
problem by allowing for clusters to be learnt jointly from the group structure
and from the label assigned to each group.Comment: 14 page
Positive recurrence of reflecting Brownian motion in three dimensions
Consider a semimartingale reflecting Brownian motion (SRBM) whose state
space is the -dimensional nonnegative orthant. The data for such a process
are a drift vector , a nonsingular covariance matrix
, and a reflection matrix that specifies the boundary
behavior of . We say that is positive recurrent, or stable, if the
expected time to hit an arbitrary open neighborhood of the origin is finite for
every starting state. In dimension , necessary and sufficient conditions
for stability are known, but fundamentally new phenomena arise in higher
dimensions. Building on prior work by El Kharroubi, Ben Tahar and Yaacoubi
[Stochastics Stochastics Rep. 68 (2000) 229--253, Math. Methods Oper. Res. 56
(2002) 243--258], we provide necessary and sufficient conditions for stability
of SRBMs in three dimensions; to verify or refute these conditions is a simple
computational task. As a byproduct, we find that the fluid-based criterion of
Dupuis and Williams [Ann. Probab. 22 (1994) 680--702] is not only sufficient
but also necessary for stability of SRBMs in three dimensions. That is, an SRBM
in three dimensions is positive recurrent if and only if every path of the
associated fluid model is attracted to the origin. The problem of recurrence
classification for SRBMs in four and higher dimensions remains open.Comment: Published in at http://dx.doi.org/10.1214/09-AAP631 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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