Automated Parameterization of Performance Models from Measurements

Giuliano Casale, Simon Spinner, Weikun Wang

Abstract: Estimating parameters of performance models from empirical measurements is a critical task, which often has a major influence on the predictive accuracy of a model. This tutorial presents the problem of parameter estimation in queueing systems and queueing networks. The focus is on reliable estimation of the arrival rates of the requests and of the service demands they place at the servers. The tutorial covers common estimation techniques such as regression methods, maximum-likelihood estimation, and moment-matching, discussing their sensitivity with respect to data and model characteristics. The tutorial also demonstrates the automated estimation of model parameters using new open source tools.