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首页> 外文会议>Proceedings of the 1st Network Traffic Measurement and Analysis Conference >Do you see me now? Sparsity in passive observations of address liveness
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Do you see me now? Sparsity in passive observations of address liveness

机译：你现在能看见我吗？被动观察地址活跃度的稀疏性

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Accurate information about address and block usage in the Internet has many applications in planning address allocation, topology studies, and simulations. Prior studies used active probing, sometimes augmented with passive observation, to study macroscopic phenomena, such as the overall usage of the IPv4 address space. This paper instead studies the completeness of passive sources: how well they can observe microscopic phenomena such as address usage within a given network. We define sparsity as the limitation of a given monitor to see a target, and we quantify the effects of interest, temporal, and coverage sparsity. To study sparsity, we introduce inverted analysis, a novel approach that uses complete passive observations of a few end networks (three campus networks in our case) to infer what of these networks would be seen by millions of virtual monitors near their traffic's destinations. Unsurprisingly, we find that monitors near popular content see many more targets and that visibility is strongly influenced by bipartite traffic between clients and servers. We are the first to quantify these effects and show their implications for the study of Internet liveness from passive observations. We find that visibility is heavy-tailed, with only 0.5% monitors seeing more than 10% of our targets' addresses, and is most affected by interest sparsity over temporal and coverage sparsity. Visibility is also strongly bipartite. Monitors of a different class than a target (e.g., a server monitor observing a client target) outperform monitors of the same class as a target in 82-99% of cases in our datasets. Finally, we find that adding active probing to passive observations greatly improves visibility of both server and client target addresses, but is not critical for visibility of target blocks. Our findings are valuable to understand limitations of existing measurement studies, and to develop methods to maximize microscopic completeness in future studies.
机译：有关Internet中地址和块使用情况的准确信息在规划地址分配，拓扑研究和模拟中具有许多应用。先前的研究使用主动探测（有时还辅以被动观察）来研究宏观现象，例如IPv4地址空间的整体使用情况。相反，本文研究了被动源的完整性：它们能够很好地观察微观现象，例如给定网络中的地址使用情况。我们将稀疏性定义为给定监视器看到目标的限制，并量化兴趣，时间和覆盖率稀疏性的影响。为了研究稀疏性，我们引入了倒置分析，这是一种新颖的方法，它使用了对几个终端网络（在本例中为三个园区网络）的完全被动观察，以推断出这些网络中的哪些内容将被其流量目的地附近的数百万虚拟监视器看到。毫不奇怪，我们发现受欢迎内容附近的监视器会看到更多目标，并且可见性受客户端和服务器之间双向流量的强烈影响。我们是第一个量化这些影响并从被动观察中显示它们对互联网活跃性研究的意义的人。我们发现可见性是重尾的，只有0.5％的监视器看到我们目标地址的10％以上，并且受时间和覆盖率稀疏性的利益稀疏性影响最大。可见性也很强。在我们的数据集中，有82％到99％的情况下，与目标不同类别的监视器（例如，服务器监视器在观察客户端目标）要优于与目标相同类别的监视器。最后，我们发现将主动探测添加到被动观察中可以极大地提高服务器和客户端目标地址的可见性，但对于目标块的可见性并不关键。我们的发现对于理解现有测量研究的局限性以及开发在未来研究中最大限度地提高微观完整性的方法具有重要意义。

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