  Since the Tor network is evolving into an infrastructure for anonymous
communication, analyzing the consequences of network growth is becoming more
relevant than ever. In particular, adding large amounts of resources may have
unintentional consequences for the system performance as well as security. To
this end, we contribute a methodology for the analysis of scaled Tor networks
that enables researchers to leverage real-world network data. Based on
historical network snapshots (consensuses), we derive and implement a model for
methodically scaling Tor consensuses. This allows researchers to apply
established research methods to scaled networks. We validate our model based on
historical data, showing its applicability. Furthermore, we demonstrate the
merits of our data-driven approach by conducting a simulation study to identify
performance impacts of scaling Tor.
