Factor analysis of Internet traffic destinations from similar source networks

Abstract

Purpose – This study aims to assess whether similar user populations in the Internet produce similar geographical traffic destination patterns on a per-country basis. Design/methodology/approach – We have collected a country-wide NetFlow trace, which encompasses the whole Spanish academic network, which comprises more than 350 institutions and one million users, during four months. Such trace comprises several similar campus networks in terms of population size and structure. To compare their behaviors, we propose a mixture model, which is primarily based on the Zipf-Mandelbrot power law to capture the heavy-tailed nature of the per-country traffic distribution. Then, factor analysis is performed to understand the relation between the response variable, number of bytes or packets per day, with dependent variables such as the source IP network, traffic direction, and country. Findings – Surprisingly, the results show that the geographical distribution is strongly dependent on the source IP network. Furthermore, even though there are thousands of users in a typical campus network, it turns out that the aggregation level which is required to observe a stable geographical pattern is even larger. Consequently, our results show a slow convergence rate to the domain of attraction of the model, specifically, we have found that at least 35 days worth of data are necessary to reach stability of the model’s estimated parameters. Practical implications – Based on these findings, conclusions drawn for one network cannot be directly extrapolated to different ones. Therefore, ISPs’ traffic measurement campaigns should include an extensive set of networks to cope with the space diversity, and also encompass a significant period of time due to the large transient time. Originality/value – Current state of the art includes some analysis of geographical patterns, but not comparisons between networks with similar populations. Such comparison can be useful for the design of Content Distribution Networks and the cost-optimization of peering agreements.