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首页> 外文学位 >Inference of network properties from active and passive measurements on wired/wireless networks: A modeling approach.
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Inference of network properties from active and passive measurements on wired/wireless networks: A modeling approach.

机译：从有线/无线网络上的主动和被动测量推论网络属性：一种建模方法。

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摘要

In this thesis, we propose several inference techniques to discover network properties for wired and wireless networks from active and passive measurements. These techniques are used to identify the existence of a dominant congested link, classify the access network type of an end host, and determine the fraction of wireless traffic in a large network.; We first propose a model-based approach that uses periodic end-end probes to identify whether a "dominant congested link" exists along an end-end path. We provide a formal yet intuitive definition of dominant congested link and present two simple hypothesis tests to identify whether such a link exists. We then present and examine several novel model-based approaches for identifying a dominant congested link that are based on interpreting probe loss as an unobserved (virtual) delay. We develop parameter inference algorithms for Hidden Markov Models (HMMs) and Markov models with a hidden dimension to infer this virtual delay. We further estimate the maximum queuing delay of the dominant congested link, once we identify that a dominant congested link exists.; We next propose a simple and efficient end-end scheme to classify an access network one of three categories: Ethernet, wireless LAN and low-bandwidth connection. Our scheme leverages off of intrinsic characteristics of the various access networks and utilizes the median and entropy of packet pair interarrival times. Extensive experiments show that our scheme obtains accurate classification results within 2 seconds.; Last, we propose a classification scheme to differentiate Ethernet and WLAN TCP flows based on measurements collected passively at the edge of a large network. This classifier computes the fraction of wireless TCP flows, and the degree of belief that a TCP flow traverses a WLAN inside the network. The core of this classifier is an iterative Bayesian inference algorithm developed to obtain the maximum likelihood estimate (MLE) of these quantities. We apply the classifier to various traces collected at the edge of the UMass campus network and infer that 11-14% of all TCP flows coming into UMass campus traverse a 802.11 wireless link within the campus. We also detect wireless usage (through the use of private routers and access points) in areas not covered by the official wireless infrastructure.
机译：在本文中，我们提出了几种推理技术，可以通过主动和被动测量发现有线和无线网络的网络属性。这些技术用于识别主要拥塞链路的存在，对终端主机的接入网络类型进行分类，并确定大型网络中无线流量的比例。我们首先提出一种基于模型的方法，该方法使用定期的端部探测来识别沿端部路径是否存在“显性拥塞链路”。我们提供了一个主要的拥塞链接的正式而直观的定义，并提出了两个简单的假设检验来识别这种链接是否存在。然后，我们介绍并检查几种基于模型的新颖方法来识别主要的拥塞链路，这些方法基于将探测损耗解释为未观察到的（虚拟）延迟。我们开发了具有隐藏维的隐马尔可夫模型（HMM）和马尔可夫模型的参数推断算法，以推断此虚拟延迟。一旦我们确定存在主要的拥塞链路，我们将进一步估计主要的拥塞链路的最大排队延迟。接下来，我们提出一种简单有效的终端方案，对接入网络进行分类，这是三类之一：以太网，无线局域网和低带宽连接。我们的方案利用了各种访问网络的固有特性，并利用了数据包对到达时间的中值和熵。大量实验表明，我们的方案在2秒内即可获得准确的分类结果。最后，我们提出了一种分类方案，以基于在大型网络边缘被动收集的测量结果来区分以太网和WLAN TCP流。该分类器计算无线TCP流量的比例，以及TCP流量穿越网络内部WLAN的置信度。该分类器的核心是开发的贝叶斯迭代迭代算法，用于获得这些量的最大似然估计（MLE）。我们将分类器应用于在UMass校园网络边缘收集的各种跟踪，并推断进入UMass校园的所有TCP流量的11-14％穿越校园内的802.11无线链接。我们还会检测官方无线基础架构未涵盖的区域中的无线使用情况（通过使用专用路由器和访问点）。

著录项

  • 作者

    Wei, Wei.;

  • 作者单位

    University of Massachusetts Amherst.;

  • 授予单位 University of Massachusetts Amherst.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 137 p.
  • 总页数 137
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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