Note on the GI/GI/1 queue with LCFS-PR observed at arbitrary times

Consider the GI/GI/1 queue with the Last-Come First-Served Preemptive-Resume service discipline. We give intuitive explanations for (i) the geometric nature of the stationary queue length distribution and (ii) the mutual independence of the residual service requirements of the customers in the queue, both considered at arbitrary time points. These distributions have previously been established in the literature by either first considering the system at arrival instants or using balance equations. Our direct arguments provide further understanding of (i) and (ii)

Sojourn times in non-homogeneous QBD processes with processor sharing

We study sojourn times of customers in a processor sharing model with a service rate that varies over time, depending on the number of customers and on the state of a random environment. An explicit expression is derived for the Laplace-Stieltjes transform of the sojourn time conditional on the state upon arrival and the amount of work brought into the system. Particular attention is given to the conditional mean sojourn time of a customer as a function of his required amount of work, and we establish the existence of an asymptote as the amount of work tends to infinity. The method of random time change is then extended to include the possibility of a varying service rate. By means of this method, we explain the well-established proportionality between the conditional mean sojourn time and required amount of work in processor sharing queues without random environment. Based on numerical experiments, we propose an approximation for the conditional mean sojourn time. Although first presented for exponentially distributed service requirements, the analysis is shown to extend to phase-type services. The service discipline of discriminatory processor sharing is also shown to fall within the framework

Asymptotically optimal parallel resource assignment with interference

Motivated by scheduling in cellular wireless networks and resource allocation in computer systems, we study a service facility with two classes of users having heterogeneous service requirement distributions. The aggregate service capacity is assumed to be largest when both classes are served in parallel, but giving preferential treatment to one of the classes may be advantageous when aiming at minimization of the number of users, or when classes have different economic values, for example. We set out to determine the allocation policies that minimize the total number of users in the system. For some particular cases we can determine the optimal policy exactly, but in general this is not analytically feasible. We then study the optimal policies in the fluid regime, which prove to be close to optimal in the original stochastic model. These policies can be characterized by either linear or exponential switching curves. We numerically compare our results with existing approximations based on optimization in the heavy-traffic regime. By simulations we show that, in general, our simple computable switching-curve strategies based on the fluid analysis perform well

Quasi-stationary analysis for queues with temporary overload

Motivated by the high variation in transmission rates for document transfer in the Internet and file down loads from web servers, we study the buffer content in a queue with a fluctuating service rate. The fluctuations are assumed to be driven by an independent stochastic process. We allow the queue to be overloaded in some of the server states. In all but a few special cases, either exact analysis is not tractable, or the dependence of system performance in terms of input parameters (such as the traffic load) is hidden in complex or implicit characterizations. Various asymptotic regimes have been considered to develop insightful approximations. In particular, the so-called quasistationary approximation has proven extremely useful under the assumption of uniform stability. We refine the quasi-stationary analysis to allow for temporary instability, by studying the “effective system load” which captures the effect of accumulated work during periods in which the queue is unstable

A spectral theory approach for extreme value analysis in a tandem of fluid queues

We consider a model to evaluate performance of streaming media over an unreliable network. Our model consists of a tandem of two fluid queues. The first fluid queue is a Markov modulated fluid queue that models the network congestion, and the second queue represents the play-out buffer. For this model the distribution of the total amount of fluid in the congestion and play-out buffer corresponds to the distribution of the maximum attained level of the first buffer. We show that, under proper scaling and when we let time go to infinity, the distribution of the total amount of fluid converges to a Gumbel extreme value distribution. From this result, we derive a simple closed-form expression for the initial play-out buffer level that provides a probabilistic guarantee for undisturbed play-out