An extensive body of research deals with estimating the correlation and the Hurst parameter of Internet traffic traces. The significance of these statistics is due to their fundamental impact on network performance. The coverage of Internet traffic traces is, however, limited since acquiring such traces is challenging with respect to, e.g., confidentiality, logging speed, and storage capacity. In this work, we investigate how the correlation of Internet traffic can be reliably estimated from random traffic samples. These samples are observed either by passive monitoring within the network, or otherwise by active packet probes at end systems. We analyze random sampling processes with different inter-sample distributions and show how to obtain asymptotically unbiased estimates from these samples. We quantify the inherent limitations that are due to limited observations and explore the influence of various parameters, such as sampling intensity, network utilization, or Hurst parameter on the estimation accuracy. We design an active probing method which enables simple and lightweight traffic sampling without support from the network. We verify our approach in a controlled network environment and present comprehensive Internet measurements. We find that the correlation exhibits properties such as long range dependence as well as periodicities and that it differs significantly across Internet paths and observation times.
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network
大规模移动流量分析对于数字基础设施配置、公共交通、活动规划和其他领域变得至关重要。然而，监控全市范围的移动流量是一个复杂而昂贵的过程，依赖于专用探头。其中一些探测器的精度或覆盖范围有限，另一些每天收集数十GB的日志，这些日志独立提供有限的见解。提取细粒度的模式涉及昂贵的测量、存储和后处理的空间聚合。在本文中，我们提出了一种移动流量超分辨率技术，克服了这些问题，通过推断狭隘的本地化的流量消费从粗糙的测量。我们从图像处理中汲取灵感，设计了一个针对移动网络的深度学习架构，它结合了Zipper网络（ZipNet）和生成对抗神经网络（GAN）模型。这使得能够唯一地捕获在广泛覆盖区域上常规监测的交通量快照（“低分辨率”）与通常在密集计算之后获得的0.05 km $^2$水平上的相应消耗（“高分辨率”）之间的时空关系。我们进行的实验与现实世界的数据集表明，拟议的ZipNet（-GAN）推断流量消耗具有显着的准确性和高达100 $\times$更高的粒度相比，标准的探测，同时优于现有的数据插值技术。据我们所知，这是第一次将超分辨率概念应用于大规模移动交通分析，我们的解决方案是第一个从粗聚合物中推断细粒度城市交通模式的解决方案。
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；Faculty of Information Technology, Macau University of Science and Technology（澳门科技大学信息技术学院）；State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）
