813 research outputs found
Separation of timescales in a two-layered network
We investigate a computer network consisting of two layers occurring in, for
example, application servers. The first layer incorporates the arrival of jobs
at a network of multi-server nodes, which we model as a many-server Jackson
network. At the second layer, active servers at these nodes act now as
customers who are served by a common CPU. Our main result shows a separation of
time scales in heavy traffic: the main source of randomness occurs at the
(aggregate) CPU layer; the interactions between different types of nodes at the
other layer is shown to converge to a fixed point at a faster time scale; this
also yields a state-space collapse property. Apart from these fundamental
insights, we also obtain an explicit approximation for the joint law of the
number of jobs in the system, which is provably accurate for heavily loaded
systems and performs numerically well for moderately loaded systems. The
obtained results for the model under consideration can be applied to
thread-pool dimensioning in application servers, while the technique seems
applicable to other layered systems too.Comment: 8 pages, 2 figures, 1 table, ITC 24 (2012
Critically loaded multi-server queues with abandonments, retrials, and time-varying parameters
In this paper, we consider modeling time-dependent multi-server queues that
include abandonments and retrials. For the performance analysis of those, fluid
and diffusion models called "strong approximations" have been widely used in
the literature. Although they are proven to be asymptotically exact, their
effectiveness as approximations in critically loaded regimes needs to be
investigated. To that end, we find that existing fluid and diffusion
approximations might be either inaccurate under simplifying assumptions or
computationally intractable. To address that concern, this paper focuses on
developing a methodology by adjusting the fluid and diffusion models so that
they significantly improve the estimation accuracy. We illustrate the accuracy
of our adjusted models by performing a number of numerical experiments
Many-server diffusion limits for queues
This paper studies many-server limits for multi-server queues that have a
phase-type service time distribution and allow for customer abandonment. The
first set of limit theorems is for critically loaded queues, where
the patience times are independent and identically distributed following a
general distribution. The next limit theorem is for overloaded
queues, where the patience time distribution is restricted to be exponential.
We prove that a pair of diffusion-scaled total-customer-count and
server-allocation processes, properly centered, converges in distribution to a
continuous Markov process as the number of servers goes to infinity. In the
overloaded case, the limit is a multi-dimensional diffusion process, and in the
critically loaded case, the limit is a simple transformation of a diffusion
process. When the queues are critically loaded, our diffusion limit generalizes
the result by Puhalskii and Reiman (2000) for queues without customer
abandonment. When the queues are overloaded, the diffusion limit provides a
refinement to a fluid limit and it generalizes a result by Whitt (2004) for
queues with an exponential service time distribution. The proof
techniques employed in this paper are innovative. First, a perturbed system is
shown to be equivalent to the original system. Next, two maps are employed in
both fluid and diffusion scalings. These maps allow one to prove the limit
theorems by applying the standard continuous-mapping theorem and the standard
random-time-change theorem.Comment: Published in at http://dx.doi.org/10.1214/09-AAP674 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Large deviations analysis for the queue in the Halfin-Whitt regime
We consider the FCFS queue in the Halfin-Whitt heavy traffic
regime. It is known that the normalized sequence of steady-state queue length
distributions is tight and converges weakly to a limiting random variable W.
However, those works only describe W implicitly as the invariant measure of a
complicated diffusion. Although it was proven by Gamarnik and Stolyar that the
tail of W is sub-Gaussian, the actual value of was left open. In subsequent work, Dai and He
conjectured an explicit form for this exponent, which was insensitive to the
higher moments of the service distribution.
We explicitly compute the true large deviations exponent for W when the
abandonment rate is less than the minimum service rate, the first such result
for non-Markovian queues with abandonments. Interestingly, our results resolve
the conjecture of Dai and He in the negative. Our main approach is to extend
the stochastic comparison framework of Gamarnik and Goldberg to the setting of
abandonments, requiring several novel and non-trivial contributions. Our
approach sheds light on several novel ways to think about multi-server queues
with abandonments in the Halfin-Whitt regime, which should hold in considerable
generality and provide new tools for analyzing these systems
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