论文标题
使用复发缓冲单元模拟网络路径
Simulating Network Paths with Recurrent Buffering Units
论文作者
论文摘要
模拟物理网络路径（例如，互联网）是AI-For-netWorking新兴子场中的基石研究问题。我们寻求一个模型，该模型可以根据发件人提供的时间变化负载来生成端到端数据包延迟值，这通常是先前输出延迟的函数。问题设置是唯一的，并且使最先进的文本和时间序列生成模型无法实现或无效。我们在动态系统，顺序决策和时间序列建模的交集中提出了ML问题。我们提出了一种新型的Grey-Box方法来用于网络模拟，该方法将物理网络路径的语义嵌入了一种称为RBU的新型RNN式模型中，从而提供了标准网络模拟器工具的可解释性，神经模型的幂，基于SGD的技术的效率，以及基于SGD的技术的效率，以及对合成和现实世界网络的合成结果产生有希望的结果。
Simulating physical network paths (e.g., Internet) is a cornerstone research problem in the emerging sub-field of AI-for-networking. We seek a model that generates end-to-end packet delay values in response to the time-varying load offered by a sender, which is typically a function of the previously output delays. The problem setting is unique, and renders the state-of-the-art text and time-series generative models inapplicable or ineffective. We formulate an ML problem at the intersection of dynamical systems, sequential decision making, and time-series modeling. We propose a novel grey-box approach to network simulation that embeds the semantics of physical network path in a new RNN-style model called RBU, providing the interpretability of standard network simulator tools, the power of neural models, the efficiency of SGD-based techniques for learning, and yielding promising results on synthetic and real-world network traces.
