论文标题
基于优化的TOR网络预测拥堵控制：机遇和挑战
Optimization-Based Predictive Congestion Control for the Tor Network: Opportunities and Challenges
论文作者
论文摘要
基于洋葱路由的原理，TOR网络通过在一系列中间继电器上继电器数据来实现其用户的匿名性。这种方法使网络中的拥塞控制成为具有挑战性的任务。截至今天，由于积压大量和不公平的数据率分配，这会导致更高的潜伏期。在本文中，我们提出了一项关于预测变量的概念研究，这是一种解决堵塞覆盖网络的新型拥塞控制方法。与传统方法不同，它建立在分布式模型预测控制的概念上，这是控制理论领域的最新进步。预测变量是为了最大程度地减少网络的延迟并实现最大公平性的量身定制。我们在两个玩具场景中对其行为进行了彻底评估，以评估优化器和复杂网络以评估其潜力。为此，我们进行了大规模的模拟研究，并将预测因子与TOR中的现有拥塞控制机制进行了比较。我们表明，预测因子在减少延迟和实现公平率分配方面非常有效。此外，我们努力将现代控制理论的思想带到网络社区，从而发展改善的未来拥塞控制。因此，我们通过这个新颖的研究方向证明了好处和问题。
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.
