Fitting heavy-tailed HTTP traces with the new stratified EM-algorithm

A typical step in the model-based evaluation of communication systems is to fit measured data to analytically tractable distributions. Due to the increased speed of today´s networks, even basic measurements, such as logging the requests at a Web server, can quickly generate large data traces with millions of entries. Employing complex fitting algorithms on such traces can take a significant amount of time. In this paper, we focus on the Expectation Maximization-based fitting of heavy- tailed distributed data to hyper-exponential distributions. We present a data aggregation algorithm which accelerates the fitting by several orders of magnitude. The employed aggregation algorithm has been derived from a sampling stratification technique and adapts dynamically to the distribution of the data. We illustrate the performance of the algorithm by applying it to empirical and artificial data traces.