  Based on the principle of onion routing, the Tor network achieves anonymity
for its users by relaying user data over a series of intermediate relays. This
approach makes congestion control in the network a challenging task. As of
today, this results in higher latencies due to considerable backlog as well as
unfair data rate allocation. In this paper, we present a concept study of
PredicTor, a novel approach to congestion control that tackles clogged overlay
networks. Unlike traditional approaches, it is built upon the idea of
distributed model predictive control, a recent advancement from the area of
control theory. PredicTor is tailored to minimizing latency in the network and
achieving max-min fairness. We contribute a thorough evaluation of its behavior
in both toy scenarios to assess the optimizer and complex networks to assess
its potential. For this, we conduct large-scale simulation studies and compare
PredicTor to existing congestion control mechanisms in Tor. We show that
PredicTor is highly effective in reducing latency and realizing fair rate
allocations. In addition, we strive to bring the ideas of modern control theory
to the networking community, enabling the development of improved, future
congestion control. We therefore demonstrate benefits and issues alike with
this novel research direction.
