PredicTor：Tor网络的预测性拥塞控制
PredicTor: Predictive Congestion Control for the Tor Network
摘要：
在Tor网络中，匿名是通过多层架构实现的，这是以复杂网络为代价的。在这种网络中调度数据是一项具有挑战性的任务，并且当前的方法显示出无法避免网络拥塞和分配公平的数据率。我们提出了Predictor，一种分布式模型预测控制方法，以应对这些挑战。PredictTor旨在调度Tor架构的各个节点上的传入和传出数据率，从而实现可扩展的方法。我们成功地避免拥塞，通过交换信息的预测行为与相邻的节点。此外，我们制定了一个专注于公平分配资源的PredictTor，我们提出并证明了一种新的基于优化的公平性方法。我们建议的控制器进行评估与流行的网络模拟器ns-3，在那里我们比较它与当前的Tor调度程序，以及与另一个最近提出的增强。PredictTor比以前的方法有了显着的改进，特别是在延迟方面。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
