Traceroute算法中随机树的Steiner子树分析
Analysis of Steiner subtrees of Random Trees for Traceroute Algorithms
摘要：
在本文中，我们考虑的问题发现，通过一个traceroute算法，网络的拓扑结构，其图形是由一个无限的分支过程。根据某个标准选择节点的子集。作为衡量算法效率的指标，研究了所选节点的Steiner距离，即这些节点的生成子树的大小。对于节点的选择，考虑两个标准：节点是随机选择的概率，这是独立的节点的深度（均匀模型），否则在深度偏置模型，是指数衰减相对于其深度。这两个模型的限制行为的大小发现的子树进行了调查。
英文摘要：
We consider in this paper the problem of discovering, via a traceroute algorithm, the topology of a network, whose graph is spanned by an infinite branching process. A subset of nodes is selected according to some criterion. As a measure of efficiency of the algorithm, the Steiner distance of the selected nodes, i.e. the size of the spanning sub-tree of these nodes, is investigated. For the selection of nodes, two criteria are considered: A node is randomly selected with a probability, which is either independent of the depth of the node (uniform model) or else in the depth biased model, is exponentially decaying with respect to its depth. The limiting behavior the size of the discovered subtree is investigated for both models.
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。
