Smoothed perturbation analysis for queues with finite buffers

Applying the technique of smoothed perturbation analysis (SPA) to theGI/G/1/K queue, we derive gradient estimators for two performance measures: the mean steady-state system time of a served customer and the probability that an arriving customer is rejected. Unbiasedness of the estimators follows from results of a previous general framework on SPA estimators. However, in that framework, the estimators often require the simulation of numerous additional sample subpaths, possibly making the technique practically infeasible in applications. We exploit some of the special structure of theGI/G/1/K queue to come up with an estimator which requires at most the simulation of a single additional sample subpath. By establishing certain regenerative properties, we provide a strong consistency proof for the estimator.     

Analysis of the MAP/PH/1/K queue with service control

We consider a finite capacity queue with Markovian arrivals, in which the service rates are controlled by two pre-determined thresholds, M and N. The service rate is increased when the buffer size exceeds N and then brought back to normal service rate when the buffer size drops to M. The normal and fast service times are both assumed to be of phase type with representations (β, S), and β θS), respectively, where θ>1. For this queueing model, steady state analysis is performed. The server duration in normal as well as fast periods is shown to be of phase type. The departure process is modelled as a MAP and the parameter matrices of the MAP are identified. Efficient algorithms for computing system performance measures are presented. We also discuss an optimization problem and present an efficient algorithm for arriving at an optimal solution. Some numerical examples are discussed.     

Discrete time analysis of MAP/PH/1 vacation queue with gated time‐limited service

We analyse a single‐server queue in which the server goes through alternating periods of vacation and work. In each work period, the server attends to the queue for no more than a fixed length of time, T. The system is a gated one in which the server, during any visit, does not attend to customers which were not in the system before its visit. As soon as all the customers within the gate have been served or the time limit has been reached (whichever occurs first) the server goes on a vacation. The server does not wait in the queue if the system is empty at its arrival for a visit. For this system the resulting Markov chain, of the queue length and some auxiliary variables, is level‐dependent. We use special techniques to carry out the steady state analysis of the system and show that when the information regarding the number of customers in the gate is not critical we are able to reduce this problem to a level‐independent Markov chain problem with large number of boundary states. For this modified system we use a hybrid method which combines matrix‐geometric method for the level‐independent part of the system with special solution method for the large complex boundary which is level‐dependent. This revised version was published online in June 2006 with corrections to the Cover Date.     

A delay decomposition approach for robust dissipativity and passivity analysis of neutral‐type neural networks with leakage time‐varying delay

This article presents the robust dissipativity and passivity analysis of neutral‐type neural networks with leakage time‐varying delay via delay decomposition approach. Using delay decomposition technique, new delay‐dependent criteria ensuring the considered system to be ‐γ dissipative are established in terms of strict linear matrix inequalities. A new Lyapunov–Krasovskii functional is constructed by dividing the discrete and neutral delay intervals into m and l segments, respectively, and choosing different Lyapunov functionals to different segments. Further, the dissipativity behaviors of neural networks which are affected due to the sensitiveness of the time delay in the leakage term have been taken into account. Finally, numerical examples are provided to show the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Complexity 21: 248–264, 2016