Traffic monitoring and estimation of flow characteristics,
such as the size and duration distributions, can be problematic when the length of an observation window is constrained (e.g., due to hard network resources). Indeed, as shown in this work, sampled flows are usually affected by censoring in an observation window, which leads to biased estimators. To account for censoring, a mathematical framework that describes sampling of flows in a time window is developed. Using censoring analysis, we provide nonparametric maximum likelihood estimators for the
flow duration and size distributions. The estimators are computed using the EM algorithm. Finally, the estimators are applied to an actual traffic trace, and are found to perform very well.

CEMAT - Center for Computational and Stochastic Mathematics