Monitoring network topology dynamism of large-scale traceroute-based measurements

Network topology discovery with distributed traceroute-based measurement systems is important to monitor, measure, diagnose and capture IP-level network topology dynamism. Depending on the discovered topology size and the captured topology dynamism accuracy, a compromise has to be done regarding the measurement time granularity and the scale of these measurement systems. In this paper, we present our large-scale measurement dataset, and analysis of the network topology dynamism captured in a real measurement scenario. We also quantify the missed dynamism information with coarser measurement time granularity inferred by our proposed algorithm. These results confirm that probing less frequently, as it is the case of most of the existing measurement systems today, can dramatically affect the dynamism information captured.