Discovering users with similar internet access performance through cluster analysis

Users typically subscribe to an Internet access service on the basis of a specific download speed, but the actual service may differ. Several projects are active collecting internet access performance measurements on a large scale at the end user location. However, less attention has been devoted to analyzing such data and to inform users on the received services. This paper presents MiND, a cluster-based methodology to analyze the characteristics of periodic Internet measurements collected at the end user location. MiND allows to discover (i) groups of users with a similar Internet access behavior and (ii) the (few) users with somehow anomalous service. User measurements over time have been modeled through histograms and then analyzed through a new two-level clustering strategy. MiND has been evaluated on real data collected by Neubot, an open source tool, voluntary installed by users, that periodically collects Internet measurements. Experimental results show that the majority of users can be grouped into homogeneous and cohesive clusters according to the Internet access service that they receive in practice, while a few users receiving anomalous services are correctly identified as outliers. Both users and ISPs can benefit from such information: users can constantly monitor the ISP offered service, whereas ISPs can quickly identify anomalous behaviors in their offered services and act accordingly.