Data mining in the analysis of Internet performance as perceived by end-users

In this paper, we propose data mining analysis of Internet performance data. The research is aimed at characterization of Web performance as seen by the end-users. Our strategy involves discovering knowledge that characterizes Web performance perceived by end-users and then making use of this knowledge to guide users in further Internet usage. Our two-phase data mining decision model can predict RTT and throughput behavior when downloading Web objects. The data is collected by the Wing measurement infrastructure. Real measurements data is used in model evaluation. We show that the number of correct classifications is high (76%-93%).