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Parametric Estimation of Tail Probabilities for the Single-Server Queue
Parametric Estimation Tail Probabilities Single-Server Queue
2015/7/8
In this chapter, we consider the question of how long the arrival process to the single-server queue needs to be observed in order to accurately estimate the long-run fraction of time that the workloa...
A Diffusion Approximation for a Markovian Queue with Reneging
Markovian queues reneging impatience deadlines refl ected Ornstein–Uhlenbeck process
2015/7/8
Consider a single-server queue with a Poisson arrival process and exponential processing times in which each customer independently reneges after an exponentially distributed amount of time. We establ...
A Diffusion Approximation with a GI/GI/1 Queue with Balking or Reneging
deadlines reneging balking impatience GI/GI/1-GI queue Ornstein-Uhlenbeck process
2015/7/6
Consider a single-server queue with a renewal arrival process and generally distributed processing times in which each customer independently reneges if service has not begun within a generally distri...
Fluid Heuristics, Lyapunov Bounds, and Efficient Importance Sampling for a Heavy-tailed G/G/1 Queue
State-dependent importance sampling Rare-event simulation Heavy-tails
2015/7/6
We develop a strongly efficient rare-event simulation algorithm for computing the tail of the steady-state waiting time in a single server queue with regularly varying service times. Our algorithm is ...
On the Dynamics of a Finite Buffer Queue Conditioned on the Amount of Loss
Dynamics Finite Buffer Queue Conditioned Amount of Loss
2015/7/6
This paper is concerned with computing large deviations asymptotics for the loss process in a stylized queueing model that is fed by a Brownian input process. In addition, the dynamics of the queue, c...
Large Deviations for the Empirical Mean of an M/M/1 Queue
Large Deviations Empirical Mean M/M/1 Queue
2015/7/6
The theory of large deviations for random walks is important both in its own right and as a starting point for establishing large deviations for more complex models (such as ìsmall noiseîdi§usion...