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生成用于链路级互联网拓扑建模的 2 模式无标度图。

Generation of 2-mode scale-free graphs for link-level internet topology modeling.

机构信息

Department of Computer Science and Engineering, University of Nevada, Reno, Nevada, United States of America.

School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States of America.

出版信息

PLoS One. 2020 Nov 9;15(11):e0240100. doi: 10.1371/journal.pone.0240100. eCollection 2020.

DOI:10.1371/journal.pone.0240100
PMID:33166286
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7652253/
Abstract

Comprehensive analysis that aims to understand the topology of real-world networks and the development of algorithms that replicate their characteristics has been significant research issues. Although the accuracy of newly developed network protocols or algorithms does not depend on the underlying topology, the performance generally depends on the topology. As a result, network practitioners have concentrated on generating representative synthetic topologies and utilize them to investigate the performance of their design in simulation or emulation environments. Network generators typically represent the Internet topology as a graph composed of point-to-point links. In this study, we discuss the implications of multi-access links on the synthetic network generation and modeling of the networks as bi-partite graphs to represent both subnetworks and routers. We then analyze the characteristics of sampled Internet topology data sets from backbone Autonomous Systems (AS) and observe that in addition to the commonly recognized power-law node degree distribution, the subnetwork size and the router interface distributions often exhibit power-law characteristics. We introduce a SubNetwork Generator (SubNetG) topology generation approach that incorporates the observed measurements to produce bipartite network topologies. In particular, generated topologies capture the 2-mode relation between the layer-2 (i.e., the subnetwork and interface distributions) and the layer-3 (i.e., the degree distribution) that is missing from the current network generators that produce 1-mode graphs. The SubNetG source code and experimental data is available at https://github.com/netml/sonet.

摘要

旨在理解真实世界网络拓扑结构以及复制其特征的算法的综合分析一直是重要的研究课题。尽管新开发的网络协议或算法的准确性不依赖于底层拓扑结构,但性能通常取决于拓扑结构。因此,网络从业者一直专注于生成具有代表性的合成拓扑结构,并利用它们在模拟或仿真环境中研究其设计的性能。网络生成器通常将互联网拓扑表示为由点对点链路组成的图。在本研究中,我们讨论了多址链路对合成网络生成和作为二部图建模网络的影响,以表示子网和路由器。然后,我们分析了骨干自治系统 (AS) 中的抽样互联网拓扑数据集的特征,并观察到除了常见的幂律节点度分布外,子网大小和路由器接口分布通常也呈现幂律特征。我们引入了一种子网生成器 (SubNetG) 拓扑生成方法,该方法结合了观察到的测量结果来生成二部网络拓扑。特别是,生成的拓扑图捕获了当前生成 1 模式图的网络生成器所缺少的 2 模式关系,即层 2(即子网和接口分布)和层 3(即度分布)之间的关系。SubNetG 的源代码和实验数据可在 https://github.com/netml/sonet 上获得。

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