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.
翻译:模拟物理网络路径(例如互联网)是新兴的AI- for-网络的子领域的基石研究问题。我们寻求一种模型,针对发送者提供的时间变化负载生成端到端包延迟值,这通常是先前产出延迟的函数。问题设置是独特的,使最先进的文本和时间序列变异模型无法适用或无效。我们在动态系统、顺序决策和时间序列模型的交叉点上设计了一个ML问题。我们提出了一个新型的网络模拟灰盒方法,将物理网络路径的语义嵌入称为RNNN型的新模型RBU,提供标准网络模拟工具的可解释性、神经模型的力量、基于 SGD 技术的学习效率,并在合成和现实世界网络痕迹上产生有希望的结果。