A Robust Statistical Estimation of Internet Traffic
Yousra Chabchoub, Christine Fricker, Fabrice M. Guillemin, Philippe H. Robert · arXiv (Cornell University) · 2009
Abstract. A new method of estimating flow characteristics in the Internet is developped in this paper. For this purpose, a new set of random variables (referred to as observables) is defined. When dealing with sampled traffic, these observables can easily be computed from sampled data. By adopting a convenient mouse/elephant dichotomy also dependent on traffic, it is shown how these variables give a robust statistical information of long flows. A mathematical framework is developed to estimate the accuracy of the method. As an application, it is shown how one can estimate the number of long TCP flows when only sampled traffic is available. The algorithm proposed is tested against experimental data collected from different types of IP traffic.