Infinitesimal perturbation analysis of a multi-stage tandem of fluid queue with additive loss feedback

Abstract

In this paper Infinitesimal Perturbation Analysis (IPA) is used to derive the gradient estimators for loss volume and queue workload in a multi-stage tandem of stochastic fluid queues with instantaneous additive loss-feedback for overall congestion control. These gradient estimators are then used to drive a standard stochastic approximation algorithm to optimize, with respect to the buffer limits of the individual queues, an objective function which is the weighted sum of loss volume and queue workload of the queues that make up the tandem.