基于加法器的网络层析成像
Network Tomography Based on Additive Metrics
摘要：
网络结构的推断（例如，路由拓扑）和动态（例如，链路性能）是许多网络设计和管理任务中的重要组成部分。在本文中，我们提出了一个新的，一般的框架，分析和设计路由拓扑结构和链路性能推理算法的思想和工具，从进化生物学的系统发育推理。该框架适用于各种计量技术。基于该框架，我们介绍和发展了几个多项式时间的距离为基础的推理算法，可证明的性能。我们提供了算法正确性的充分条件。我们表明，该算法是一致的（返回正确的拓扑结构和链路性能，随着样本量的增加）和鲁棒性（可以容忍一定程度的测量误差）。此外，我们建立了算法的某些最优性性质（即，它们实现了最优的$l_\infty$ -半径），并通过模型仿真证明了它们的有效性。
英文摘要：
Inference of the network structure (e.g., routing topology) and dynamics (e.g., link performance) is an essential component in many network design and management tasks. In this paper we propose a new, general framework for analyzing and designing routing topology and link performance inference algorithms using ideas and tools from phylogenetic inference in evolutionary biology. The framework is applicable to a variety of measurement techniques. Based on the framework we introduce and develop several polynomial-time distance-based inference algorithms with provable performance. We provide sufficient conditions for the correctness of the algorithms. We show that the algorithms are consistent (return correct topology and link performance with an increasing sample size) and robust (can tolerate a certain level of measurement errors). In addition, we establish certain optimality properties of the algorithms (i.e., they achieve the optimal $l_\infty$-radius) and demonstrate their effectiveness via model simulation.
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。
