Cluster Analysis of Internet Users Based on Hourly Traffic Utilization

Internet access traffic follows hourly patterns that depend on various factors, such as the periods users stay in the access point (e.g. at home or in the office) or their preferences for applications. The clustering of Internet users may provide important information for traffic engineering and tariffing. For example, it can be used to set up service differentiation according to hourly behavior, resource optimization based on multi-hour routing and definition of tariffs that promote Internet access in low busy hours. In this work, we identify patterns of similar behavior by grouping Internet users of two distinct Portuguese ISPs, one using a CATV access network and the other an ADSL one and offering distinct traffic contracts. The grouping of the users is based on their traffic utilization measured every half-hour. Cluster analysis is used to identify the relevant Internet usage profiles, with the partitioning around medoids and Ward’s method being the preferred clustering methods. For the two data sets, these clustering methods lead to 3 clusters with similar hourly traffic utilization profiles. The cluster structure is validated through discriminant analysis. Having identified the clusters, the type of applications used as well as the flow duration and transfer rate are analyzed for each cluster resulting in coherent outcomes.