论文标题
预测因子：TOR网络的预测交通总结控制
PredicTor: Predictive Congestion Control for the Tor Network
论文作者
论文摘要
在TOR网络中，通过多层体系结构来实现匿名性，该体系结构以复杂的网络为代价。在此网络中安排数据是一项具有挑战性的任务，当前的方法表明无法避免网络拥塞和分配公平数据速率。我们提出了一种分布式模型预测控制方法的预测变量，以应对这些挑战。预测因子旨在安排TOR体系结构各个节点的传入和传出数据速率，从而导致可扩展方法。我们通过与相邻节点交换预测行为的信息成功地避免了交通拥堵。此外，我们以关注资源的公平分配来制定预测指标，并证明一种新颖的基于优化的公平方法。我们提出的控制器通过流行的网络模拟器NS-3进行评估，我们将其与当前的TOR调度程序以及另一个最近提出的增强作用进行了比较。预测因子比以前的方法显示出显着改善，尤其是在潜伏期方面。
In the Tor network, anonymity is achieved through a multi-layered architecture, which comes at the cost of a complex network. Scheduling data in this network is a challenging task and the current approach shows to be incapable of avoiding network congestion and allocating fair data rates. We propose PredicTor, a distributed model predictive control approach, to tackle these challenges. PredicTor is designed to schedule incoming and outgoing data rates on individual nodes of the Tor architecture, leading to a scalable approach. We successfully avoid congestion through exchanging information of predicted behavior with adjacent nodes. Furthermore, we formulate PredicTor with a focus on fair allocation of resources, for which we present and proof a novel optimization-based fairness approach. Our proposed controller is evaluated with the popular network simulator ns-3, where we compare it with the current Tor scheduler as well as with another recently proposed enhancement. PredicTor shows significant improvements over the previous approaches, especially with respect to latency.
