Reducing Network Traffic Data Sets

In the study of network traffic, the collection and the processing of measurement data sets play a fundamental role. Due to the large size of typical traffic traces, their analysis is often heavy in terms of computational time and resources. In addition, even when the data sets are small, due to the intrinsic redundancy of the data, there is no need to consider the entire data sets in the processing stages. To cope with these issues, we use an entropy-based methodology to reduce network traffic data sets obtained by measurements over real networks. The off-line approach we used is based on the marginal utility concept, and reveals encouraging results when applied to real data captured over real networks, especially when dealing with large amounts of data. To show the applicability of our approach, we present and discuss results obtained in the analysis and characterization, at packet-level, of traffic traces from two popular network games: Counter-Strike and Age of Mythology. Thanks to the differences between the two considered on-line games and their traffic traces we can draw pros and cons in realistic scenarios.