Exact gradient simulation for stochastic fluid networks in steady state

In this paper, we develop a new simulation algorithm that generates unbiased gradient estimators for the steady-state workload of a stochastic fluid network, with respect to the throughput rate of each server. Our algorithm is based on the perfect sampling algorithm developed in Blanchet and Chen (2014), and the infinitesimal perturbation analysis (IPA) method. We illustrate the performance of our algorithm with two multidimensional examples, including its formal application in the case of multidimensional reflected Brownian motion.