26 research outputs found
Optimal Paths in Large Deviations of Symmetric Reflected Brownian Motion in the Octant
We study the variational problem that arises from consideration of large
deviations for semimartingale reflected Brownian motion (SRBM) in the positive
octant. Due to the difficulty of the general problem, we consider the case in
which the SRBM has rotationally symmetric parameters. In this case, we are able
to obtain conditions under which the optimal solutions to the variational
problem are paths that are gradual (moving through faces of strictly increasing
dimension) or that spiral around the boundary of the octant. Furthermore, these
results allow us to provide an example for which it can be verified that a
spiral path is optimal. For rotationally symmetric SRBM's, our results
facilitate the simplification of computational methods for determining optimal
solutions to variational problems and give insight into large deviations
behavior of these processes
Stability of Skorokhod problem is undecidable
Skorokhod problem arises in studying Reflected Brownian Motion (RBM) on an
non-negative orthant, specifically in the context of queueing networks in the
heavy traffic regime. One of the key problems is identifying conditions for
stability of a Skorokhod problem, defined as the property that trajectories are
attracted to the origin. The stability conditions are known in dimension up to
three, but not for general dimensions.
In this paper we explain the fundamental difficulties encountered in trying
to establish stability conditions for general dimensions. We prove that
stability of Skorokhod problem is an undecidable property when the starting
state is a part of the input. Namely, there does not exist an algorithm (a
constructive procedure) for identifying stable Skorokhod problem in general
dimensions
Modeling a healthcare system as a queueing network:The case of a Belgian hospital.
The performance of health care systems in terms of patient flow times and utilization of critical resources can be assessed through queueing and simulation models. We model the orthopaedic department of the Middelheim hospital (Antwerpen, Belgium) focusing on the impact of outages (preemptive and nonpreemptive outages) on the effective utilization of resources and on the flowtime of patients. Several queueing network solution procedures are developed such as the decomposition and Brownian motion approaches. Simulation is used as a validation tool. We present new approaches to model outages. The model offers a valuable tool to study the trade-off between the capacity structure, sources of variability and patient flow times.Belgium; Brownian motion; Capacity management; Decomposition; Health care; Healthcare; Impact; Model; Models; Performance; Performance measurement; Queueing; Queueing theory; Simulation; Stochastic processes; Structure; Studies; Systems; Time; Tool; Validation; Variability;
Asymptotic expansion of stationary distribution for reflected Brownian motion in the quarter plane via analytic approach
Brownian motion in R 2 + with covariance matrix and drift in
the interior and reflection matrix R from the axes is considered. The
asymptotic expansion of the stationary distribution density along all paths in
R 2 + is found and its main term is identified depending on parameters
(, , R). For this purpose the analytic approach of Fayolle,
Iasnogorodski and Malyshev in [12] and [36], restricted essentially up to now
to discrete random walks in Z 2 + with jumps to the nearest-neighbors in the
interior is developed in this article for diffusion processes on R 2 + with
reflections on the axes
Heavy-traffic asymptotics for networks of parallel queues with Markov-modulated service speeds
We study a network of parallel single-server queues, where the speeds of the servers are varying over time and governed by a single continuous-time Markov chain. We obtain heavy-traf¿c limits for the distributions of the joint workload, waiting time and queue length processes. We do so by using a functional central limit theorem approach, which requires the interchange of steady-state and heavy-traf¿c limits. The marginals of these limiting distributions are shown to be exponential with rates that can be computed by matrix-analytic methods. Moreover, we show how to numerically compute the joint distributions, by viewing the limit processes as multi-dimensional semi-martingale re¿ected Brownian motions in the non-negative orthant
Reversibility in Queueing Models
In stochastic models for queues and their networks, random events evolve in
time. A process for their backward evolution is referred to as a time reversed
process. It is often greatly helpful to view a stochastic model from two
different time directions. In particular, if some property is unchanged under
time reversal, we may better understand that property. A concept of
reversibility is invented for this invariance. Local balance for a stationary
Markov chain has been used for a weaker version of the reversibility. However,
it is still too strong for queueing applications.
We are concerned with a continuous time Markov chain, but dose not assume it
has the stationary distribution. We define reversibility in structure as an
invariant property of a family of the set of models under certain operation.
The member of this set is a pair of transition rate function and its supporting
measure, and each set represents dynamics of queueing systems such as arrivals
and departures. We use a permutation {\Gamma} of the family menmbers, that is,
the sets themselves, to describe the change of the dynamics under time
reversal. This reversibility is is called {\Gamma}-reversibility in structure.
To apply these definitions, we introduce new classes of models, called
reacting systems and self-reacting systems. Using those definitions and models,
we give a unified view for queues and their networks which have reversibility
in structure, and show how their stationary distributions can be obtained. They
include symmetric service, batch movements and state dependent routing.Comment: Submitted for publicatio
Qualitative properties of -fair policies in bandwidth-sharing networks
We consider a flow-level model of a network operating under an -fair
bandwidth sharing policy (with ) proposed by Roberts and
Massouli\'{e} [Telecomunication Systems 15 (2000) 185-201]. This is a
probabilistic model that captures the long-term aspects of bandwidth sharing
between users or flows in a communication network. We study the transient
properties as well as the steady-state distribution of the model. In
particular, for , we obtain bounds on the maximum number of flows
in the network over a given time horizon, by means of a maximal inequality
derived from the standard Lyapunov drift condition. As a corollary, we
establish the full state space collapse property for all . For the
steady-state distribution, we obtain explicit exponential tail bounds on the
number of flows, for any , by relying on a norm-like Lyapunov
function. As a corollary, we establish the validity of the diffusion
approximation developed by Kang et al. [Ann. Appl. Probab. 19 (2009)
1719-1780], in steady state, for the case where and under a local
traffic condition.Comment: Published in at http://dx.doi.org/10.1214/12-AAP915 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