1,372 research outputs found
On the infimum attained by a reflected L\'evy process
This paper considers a L\'evy-driven queue (i.e., a L\'evy process reflected
at 0), and focuses on the distribution of , that is, the minimal value
attained in an interval of length (where it is assumed that the queue is in
stationarity at the beginning of the interval). The first contribution is an
explicit characterization of this distribution, in terms of Laplace transforms,
for spectrally one-sided L\'evy processes (i.e., either only positive jumps or
only negative jumps). The second contribution concerns the asymptotics of
\prob{M(T_u)> u} (for different classes of functions and large);
here we have to distinguish between heavy-tailed and light-tailed scenarios
Low latency via redundancy
Low latency is critical for interactive networked applications. But while we
know how to scale systems to increase capacity, reducing latency --- especially
the tail of the latency distribution --- can be much more difficult. In this
paper, we argue that the use of redundancy is an effective way to convert extra
capacity into reduced latency. By initiating redundant operations across
diverse resources and using the first result which completes, redundancy
improves a system's latency even under exceptional conditions. We study the
tradeoff with added system utilization, characterizing the situations in which
replicating all tasks reduces mean latency. We then demonstrate empirically
that replicating all operations can result in significant mean and tail latency
reduction in real-world systems including DNS queries, database servers, and
packet forwarding within networks
Distribution of the time at which the deviation of a Brownian motion is maximum before its first-passage time
We calculate analytically the probability density of the time
at which a continuous-time Brownian motion (with and without drift) attains its
maximum before passing through the origin for the first time. We also compute
the joint probability density of the maximum and . In the
driftless case, we find that has power-law tails: for large and for small . In
presence of a drift towards the origin, decays exponentially for large
. The results from numerical simulations are in excellent agreement with
our analytical predictions.Comment: 13 pages, 5 figures. Published in Journal of Statistical Mechanics:
Theory and Experiment (J. Stat. Mech. (2007) P10008,
doi:10.1088/1742-5468/2007/10/P10008
Measurement-Adaptive Cellular Random Access Protocols
This work considers a single-cell random access channel (RACH) in cellular
wireless networks. Communications over RACH take place when users try to
connect to a base station during a handover or when establishing a new
connection. Within the framework of Self-Organizing Networks (SONs), the system
should self- adapt to dynamically changing environments (channel fading,
mobility, etc.) without human intervention. For the performance improvement of
the RACH procedure, we aim here at maximizing throughput or alternatively
minimizing the user dropping rate. In the context of SON, we propose protocols
which exploit information from measurements and user reports in order to
estimate current values of the system unknowns and broadcast global
action-related values to all users. The protocols suggest an optimal pair of
user actions (transmission power and back-off probability) found by minimizing
the drift of a certain function. Numerical results illustrate considerable
benefits of the dropping rate, at a very low or even zero cost in power
expenditure and delay, as well as the fast adaptability of the protocols to
environment changes. Although the proposed protocol is designed to minimize
primarily the amount of discarded users per cell, our framework allows for
other variations (power or delay minimization) as well.Comment: 31 pages, 13 figures, 3 tables. Springer Wireless Networks 201
Quantum algorithm and circuit design solving the Poisson equation
The Poisson equation occurs in many areas of science and engineering. Here we
focus on its numerical solution for an equation in d dimensions. In particular
we present a quantum algorithm and a scalable quantum circuit design which
approximates the solution of the Poisson equation on a grid with error
\varepsilon. We assume we are given a supersposition of function evaluations of
the right hand side of the Poisson equation. The algorithm produces a quantum
state encoding the solution. The number of quantum operations and the number of
qubits used by the circuit is almost linear in d and polylog in
\varepsilon^{-1}. We present quantum circuit modules together with performance
guarantees which can be also used for other problems.Comment: 30 pages, 9 figures. This is the revised version for publication in
New Journal of Physic
Implied volatility of basket options at extreme strikes
In the paper, we characterize the asymptotic behavior of the implied
volatility of a basket call option at large and small strikes in a variety of
settings with increasing generality. First, we obtain an asymptotic formula
with an error bound for the left wing of the implied volatility, under the
assumption that the dynamics of asset prices are described by the
multidimensional Black-Scholes model. Next, we find the leading term of
asymptotics of the implied volatility in the case where the asset prices follow
the multidimensional Black-Scholes model with time change by an independent
increasing stochastic process. Finally, we deal with a general situation in
which the dependence between the assets is described by a given copula
function. In this setting, we obtain a model-free tail-wing formula that links
the implied volatility to a special characteristic of the copula called the
weak lower tail dependence function
A Markovian event-based framework for stochastic spiking neural networks
In spiking neural networks, the information is conveyed by the spike times,
that depend on the intrinsic dynamics of each neuron, the input they receive
and on the connections between neurons. In this article we study the Markovian
nature of the sequence of spike times in stochastic neural networks, and in
particular the ability to deduce from a spike train the next spike time, and
therefore produce a description of the network activity only based on the spike
times regardless of the membrane potential process.
To study this question in a rigorous manner, we introduce and study an
event-based description of networks of noisy integrate-and-fire neurons, i.e.
that is based on the computation of the spike times. We show that the firing
times of the neurons in the networks constitute a Markov chain, whose
transition probability is related to the probability distribution of the
interspike interval of the neurons in the network. In the cases where the
Markovian model can be developed, the transition probability is explicitly
derived in such classical cases of neural networks as the linear
integrate-and-fire neuron models with excitatory and inhibitory interactions,
for different types of synapses, possibly featuring noisy synaptic integration,
transmission delays and absolute and relative refractory period. This covers
most of the cases that have been investigated in the event-based description of
spiking deterministic neural networks
A L\'evy input fluid queue with input and workload regulation
We consider a queuing model with the workload evolving between consecutive
i.i.d.\ exponential timers according to a
spectrally positive L\'evy process that is reflected at zero, and
where the environment equals 0 or 1. When the exponential clock
ends, the workload, as well as the L\'evy input process, are modified; this
modification may depend on the current value of the workload, the maximum and
the minimum workload observed during the previous cycle, and the environment
of the L\'evy input process itself during the previous cycle. We analyse
the steady-state workload distribution for this model. The main theme of the
analysis is the systematic application of non-trivial functionals, derived
within the framework of fluctuation theory of L\'evy processes, to workload and
queuing models
An approximation approach for the deviation matrix of continuous-time Markov processes with application to Markov decision theory
We present an update formula that allows the expression of the deviation matrix of a continuous-time Markov process with denumerable state space having generator matrix Q* through a continuous-time Markov process with generator matrix Q. We show that under suitable stability conditions the algorithm converges at a geometric rate. By applying the concept to three different examples, namely, the M/M/1 queue with vacations, the M/G/1 queue, and a tandem network, we illustrate the broad applicability of our approach. For a problem in admission control, we apply our approximation algorithm toMarkov decision theory for computing the optimal control policy. Numerical examples are presented to highlight the efficiency of the proposed algorithm. © 2010 INFORMS
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