神经网络层析成像
Neural Network Tomography
摘要：
网络断层扫描是网络监测领域的一个经典研究课题，它是指利用选定的端到端路径测量来推断未测量的网络属性的方法。在研究界，网络断层扫描通常是在已知的网络拓扑结构，相关的路径测量，有限数量的故障节点/链路，甚至特殊的网络协议支持的假设下进行研究。网络断层扫描的适用性受到这些强假设的限制，因此经常将其定位在理论世界中。在这方面，我们从实践的角度重新审视网络断层扫描，建立一个通用的框架，不依赖于任何这些假设或性能指标的类型。仅给定采样节点对的端到端路径性能指标，所提出的框架NeuTomography利用深度神经网络和数据增强来通过学习节点对之间的非线性关系和潜在的未知拓扑/路由属性来预测未测量的性能指标。此外，NeuTomography可以用来重建原始网络拓扑结构，这对大多数网络规划任务至关重要。使用真实网络数据进行的大量实验表明，与基线解决方案相比，NeuTomography可以预测网络特性并重建网络拓扑，仅使用有限的测量数据就具有更高的准确性和鲁棒性。
英文摘要：
Network tomography, a classic research problem in the realm of network monitoring, refers to the methodology of inferring unmeasured network attributes using selected end-to-end path measurements. In the research community, network tomography is generally investigated under the assumptions of known network topology, correlated path measurements, bounded number of faulty nodes/links, or even special network protocol support. The applicability of network tomography is considerably constrained by these strong assumptions, which therefore frequently position it in the theoretical world. In this regard, we revisit network tomography from the practical perspective by establishing a generic framework that does not rely on any of these assumptions or the types of performance metrics. Given only the end-to-end path performance metrics of sampled node pairs, the proposed framework, NeuTomography, utilizes deep neural network and data augmentation to predict the unmeasured performance metrics via learning non-linear relationships between node pairs and underlying unknown topological/routing properties. In addition, NeuTomography can be employed to reconstruct the original network topology, which is critical to most network planning tasks. Extensive experiments using real network data show that comparing to baseline solutions, NeuTomography can predict network characteristics and reconstruct network topologies with significantly higher accuracy and robustness using only limited measurement data.
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。
