Network prefix-level traffic profiling: Characterizing, modeling, and evaluation

A cardinal prerequisite for the proper and efficient management of a network, especially an ISP network, is to understand the traffic that it carries. Traffic profiling is a means to obtain knowledge of the traffic behavior. Previous work has been focusing on traffic profiling at the link level or the host level. However, network prefix-level traffic behaviors have not yet been investigated. In this paper, we are interested in empirical studies for finding and describing structural patterns in the overwhelming network measurement data, as well as obtaining insight from it, with the expected traffic profiles potentially of interest to a broad range of applications such as network management, traffic engineering, and data services. To this end, first, we derive a collection of features that characterize the network prefix-level aggregate traffic behaviors. Next we use a simple model to capture them on all features, and apply machine learning techniques to extract representative profiles from them. Finally, we collect Netflow measurements from the entire periphery of a Tier-1 ISP network to empirically validate the simple model we proposed. Our extensive results show that nearly all networks exhibit traffic characteristics that are stable over time. The derived traffic profiles provide valuable insights on the manifold behavioral patterns that cannot be easily learned otherwise.