网络层析中子图的优化一致合并与剪枝
Optimizing Consistent Merging and Pruning of Subgraphs in Network Tomography
摘要：
通信网络可以被建模为具有边权重的有向连通图，边权重表征诸如损耗和延迟的性能度量。网络断层扫描的目的是推断这些边的权重，从他们的路径版本上测量的一组相交的路径之间的边界顶点的子集，甚至底层图时，这是未知的。最近的工作已经建立了条件，在该条件下，底层的有向图可以精确地恢复成对路径相关数据，即，到每个端点和来自每个端点的每对有向路径的交集的权重的集合。从数学上讲，这使我们能够一致地融合往返于每个端点的网络路径集的基于树的视图，以重建底层网络。然而，在实践中，PCD并不一致地由路径测量确定。统计波动引起不一致的推断权的边缘从测量的基础上不同的端点，同步的操作约束，并从底层的数据包传输模型的偏差。此外，用于消除噪声的自组织解决方案（诸如修剪小权重推断链接）难以以保持已知端到端度量值的一致方式应用。本文对不一致权估计问题作了统一的处理。我们制定了两种类型的不一致：\textsl{intrinsic}，当权重集是内部不一致的，和\textsl{extrinsic}，当它们与一组已知的端到端路径度量不一致。在这两种情况下，我们映射不一致的权重一致的PCD内的最小二乘框架。我们评估的性能，这种映射的组成与基于树的推理算法。
英文摘要：
A communication network can be modeled as a directed connected graph with edge weights that characterize performance metrics such as loss and delay. Network tomography aims to infer these edge weights from their pathwise versions measured on a set of intersecting paths between a subset of boundary vertices, and even the underlying graph when this is not known. Recent work has established conditions under which the underlying directed graph can be recovered exactly the pairwise Path Correlation Data, namely, the set of weights of intersection of each pair of directed paths to and from each endpoint. Algorithmically, this enables us to consistently fused tree-based view of the set of network paths to and from each endpoint to reconstruct the underlying network. However, in practice the PCD is not consistently determined by path measurements. Statistical fluctuations give rise to inconsistent inferred weight of edges from measurement based on different endpoints, as do operational constraints on synchronization, and deviations from the underlying packet transmission model. Furthermore, ad hoc solutions to eliminate noise, such as pruning small weight inferred links, are hard to apply in a consistent manner that preserves known end-to-end metric values. This paper takes a unified approach to the problem of inconsistent weight estimation. We formulate two type of inconsistency: \textsl{intrinsic}, when the weight set is internally inconsistent, and \textsl{extrinsic}, when they are inconsistent with a set of known end-to-end path metrics. In both cases we map inconsistent weight to consistent PCD within a least-squares framework. We evaluate the performance of this mapping in composition with tree-based inference algorithms.
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