Perturbation analysis and control of two-class stochastic fluid models for communication networks

This paper uses stochastic fluid models (SFMs) for the control and optimization (rather than performance analysis) of communication network nodes processing two classes of traffic: one is uncontrolled and the other is subject to threshold-based buffer control. We derive gradient estimators for packet loss and workload related performance metrics with respect to threshold parameters. These estimators are shown to be unbiased and directly observable from a sample path without any knowledge of underlying stochastic characteristics of the traffic processes. This renders them computable in online environments and easily implementable for network management and control. We further demonstrate their use in buffer control problems where our SFM-based estimators are evaluated based on data from an actual system.