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arXiv 1007.3336cs.NI
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基于网络编码的主动拓扑推理

Active Topology Inference using Network Coding

Pegah Sattari, Christina Fragouli, Athina Markopoulou

摘要:

我们的目标是推断网络的拓扑结构,当(i)我们可以在网络边缘的源和接收器之间发送探测,以及(ii)中间结点可以执行简单的网络编码操作,即,的增订条文我们的关键直觉是,网络编码在接收器的观测中引入了拓扑相关性,可以利用它来推断拓扑。对于无向树拓扑结构,我们设计层次聚类算法,建立在我们以前的工作。对于有向无环图(DAG),首先我们将拓扑结构分解为一些两个源,两个接收器(2 × 2)的子网络组件,然后我们合并这些组件来重建拓扑结构。我们的DAG方法建立在以前的工作断层扫描,并通过采用网络编码来准确区分所有不同的2乘2组件。我们评估我们的算法,通过模拟一些现实的拓扑结构,并比较它们的主动层析成像技术,没有网络编码。我们还在我们的方法和替代方案之间建立了联系,包括被动推理,traceroute和数据包标记。

英文摘要:

Our goal is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two-source, two-receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography, and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and alternatives, including passive inference, traceroute, and packet marking.

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。

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