Optimization-Based Predictive Congestion Control for the Tor Network: Opportunities and Challenges

Abstract

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 this writing, this results in higher latencies due to considerable backlog as well as unfair data rate allocation. In this article, 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. Thus, we demonstrate benefits and issues alike with this novel research direction.