We use Stochastic Fluid Models (SFM) for control and optimization (rather than performance analysis) of communication networks, focusing on problems of admission control. We consider a SFM with an uncontrolled traffic class and a controlled traffic class subject to threshold-based admission 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. This renders them computable in on-line environments and easily implementable for network management and control. We further demonstrate their use in admission control problems where our SFM-based estimators are evaluated based on data from an actual system.