Title: Perfect sampling and gradient simulation of Queueing Networks. Abstract: Perfect sampling is a Monte Carlo technique to generate samples from the stationary distribution of Markov processes without any bias. We develop a perfect sampling algorithm for a class of queueing models called stochastic fluid networks, as used in communication network and data processing systems. Our framework can be combined with infinitesimal perturbation analysis to simulate the gradient of the stationary queue length with no bias. Therefore, our perfect sampling algorithm can be used in sensitivity analysis and simulation optimization for resource allocation in the network. In the end, we will discuss the potential extension of our algorithm to reflected Brownian motion and generalized Jackson network.