SCS

Abstract

Estimations of Internet loss rates from measurements often have large statistical errors. Any meaningful application of Internet loss measurement therefore needs to incorporate these uncertainties in the results. However, estimating measurement errors is difficult as it requires the knowledge of the loss process itself. Many experiments use a simple and unrealistic Bernoulli model for the loss process and severely underestimate these errors.

Here we develop SAIL a measurement tool that can accurately estimate not only the loss rate of an end-to-end path but also its statistical errors. The key idea in our method is to capture the correlation between packet losses using an ON/OFF renewal model and to use a Hidden Semi- Markov Model algorithm to infer the parameters of the ON and OFF periods from measurement data. Once the model parameters are known, statistical properties of the loss process such as the loss rate, its variance, and the distribution of the loss and no-loss bursts can be easily computed.

This is co-work with Hung Nguyen.