Drive-thru-Internet is a scenario in cooperative intelligent transportation systems (C-ITSs), where a road-side unit (RSU) provides multimedia services to vehicles that pass by. Performance of the drive-thru-Internet depends on various factors, including data traffic intensity, vehicle traffic density, and radio-link quality within the coverage area of the RSU, and must be evaluated at the stage of system design in order to fulfill the quality-of-service requirements of the customers in C-ITS. In this paper, we present an analytical framework that models downlink traffic in a drive-thru-Internet scenario by means of a multidimensional Markov process: the packet arrivals in the RSU buffer constitute Poisson processes and the transmission times are exponentially distributed. Taking into account the state space explosion problem associated with multidimensional Markov processes, we use iterative perturbation techniques to calculate the stationary distribution of the Markov chain. Our numerical results reveal that the proposed approach yields accurate estimates of various performance metrics, such as the mean queue content and the mean packet delay for a wide range of workloads.

This paper considers appointment scheduling for outpatient services when the service of scheduled patients can be interrupted by emergency arrivals.We consider a single doctor who consults K patients during a fixed-length session.Each patient has been given an appointment time during the session in advance.Our evaluation approach aims at obtaining accurate predictions at a very low computational cost for the waiting times of the patients and the idle time of the doctor.To this end, we investigate a modified Lindley recursion in a discrete-time framework.We assume general, possibly distinct, distributions for the patient's consultation times and allow for individual no-show probabilities.This fast evaluation method is then used in a local search algorithm to provide insights into scheduling with service interruptions.Numerical examples show that this method outperforms simulation optimization and naive approaches in terms of cost and running time.

This paper considers a network of infinite-server queues with the special feature that, triggered by specific events, the network population vector may undergo a linear transformation (a ‘multiplicative transition’). For this model we characterize the joint probability generating function in terms of a system of partial differential equations; this system enables the evaluation of (transient as well as stationary) moments. We show that several relevant systems fit in the framework developed, such as networks of retrial queues, networks in which jobs can be rerouted when links fail, and storage systems. Numerical examples illustrate how our results can be used to support design problems.

This paper studies a power control game over a collision channel. Each player has an energy state and balances energy conservation and transmission success. When opting for higher transmission power, the chances of a successful transmission in the presence of interference increases at the cost of a larger drop in energy. We study this dynamic game when restricting to simple non-dynamic strategies: a power level is chosen at start-up and maintained during the lifetime of the battery. A thorough analysis of the existence and characterization of the equilibria of this evolutionary Hawk-Dove game is conducted. Moreover, we study the stability of our results under various classes of evolutionary dynamics, including replicator dynamics and Brown-von Neumann-Nash (BNN) dynamics and identify various surprising paradoxes. Simulation results validate our theoretical claims.

In this paper, we study a power control game over a collision channel. Each player has an energy state. When choosing a higher transmission power, the chance of a successful transmission (in the presence of other interference) increases at the cost of a larger decrease in the energy state of the battery. We study this dynamic game when restricting to simple non-dynamic strategies that consist of choosing a given power level that is maintained during the lifetime of the battery. We identify a surprising paradox in our Hawk-Dove game which we term the initial energy paradox.

Both optical packet switching and optical burst switching provide viable alternatives to the current electronic switching in the backbone. To resolve contention, contiguous packets/bursts are sent over different channels, and/or provided with different Fiber Delay Line (FDL) buffer delays. Typically, the resulting channel and delay selection (CDS) algorithm bases its decision on the horizon value of the different channels. Up to now, a horizon algorithm known as MING (MINimal Gap) was assumed to provide minimal loss. Relying on Markov chain-based performance modeling and a Markov decision process (MDP), we reexamine the optimality of MING. This enables us to construct CDS algorithms that outperform MING, and this for any buffer size, traffic load and packet/burst size distribution. Further, we consider burst-size-dependent and load-dependent scheduling, two stochastic mechanisms that, together with preventive dropping, enhance the algorithm's performance. The paper presents the developed method in detail, together with detailed results of the performance gain realized.

In optical burst switching and optical packet switching, contention of bursts (or packets) can be dealt with most effectively through a combination of wavelength conversion and optical buffering. While this is generally accepted in the optical networking community, and validated through simulation, analytic performance results for optical buffers were limited to the single-wavelength case, and the performance gain from wavelength conversion was never traced analytically for general assumptions. Quantifying this gain analytically is the scope of the current contribution. Relying on generating functions, we developed a fiber delay line (FDL) buffer model with wavelength conversion, which assumes the buffer located at the output of an optical switch, having access to multiple wavelengths. This document presents our model, validates its accuracy, and compares its output for different burst sizes (fixed or varying), scheduling policies and buffer sizes. Several numerical examples assess the applicability of our approximation, and show that our approach yields accurate results.

We propose a discrete-time queueing model for the evaluation of the IEEE 802.16e sleep-mode mechanism of Power Saving Class (PSC) I in wireless access networks. Contrary to previous studies, we model the downlink traffic by means of a Discrete Batch Markov Arrival Process (D-BMAP) with Ν phases, which allows to take traffic correlation into account. The tradeoff between energy saving and increased packet delay is discussed. In many situations, the sleep-mode performance improves for heavily correlated traffic. Also, when compared to other strategies, the exponential sleep-period update strategy of PSC I may not always be the best.

We present a performance model for fiber delay line (FDL) buffers having access to multiple wavelengths on an output fiber. In optical burst switching and optical packet switching, contending bursts (or packets) need to be dealt with in an effective way, and both wavelength conversion and optical buffering are viable solutions. The buffer studied here includes both solutions, with full wavelength conversion. It is situated at the output, and handles independent arrivals. We apply an analytic discrete-time queueing model to evaluate performance in terms of loss. We mainly consider the impact of burst size (fixed or varying), scheduling policy, and buffer size. Several numerical examples assess the accuracy of our approximation, and show that our approach is applicable when burst sizes are fixed, and, when a round-robin scheduling policy is adopted, also when burst sizes vary.