IPA gradient estimation for loss measures in networks of continuous flow models

This talk considers loss-related performance measures in high-speed networks from the standpoint of gradient estimation by the Infinitesimal Perturbation Analysis (IPA) technique. The underlying model consists of an interconnected system of continuous flow models (CFMs) processing data in a fluid form, and the performance measures of interest are related to the average loss volume caused by buffer overflow over a period of time. The main results presented are: (i) an easily implementable, distributed algorithm for computing the IPA derivative, and (ii) analysis of the algorithm that proves the unbiasedness of the IPA estimator. ´Me distributed algorithm can naturally be implemented in real-time at a network´s nodes, and hence potentially can be used for on-line control. The analysis highlights some of the special structure of CFMs, and suggests a general scope of 1PRs unbiasedness, far beyond the specific performance measures discussed. The talk will conclude with remarks about the potential applicability of IPA as a design and analysis tool for networks of CFMs.