A massive and growing part of Autonomous System (AS)-level traffic exchanges takes place at Internet Exchange Points (IXPs). This paper leverages PeeringDB, a database providing a partial but reasonable view of the global interconnection of ASes at IXPs, to model a complex graph enabling the characterization of the key Internet peering players and their interactions over time. We model a PeeringDB snapshot as a weighted directed bipartite graph, called the pDB c-graph, that captures the port size ASes possess at IXPs using available metadata. This novel model of the Internet is shown to picture relevant features of a complex network that groups ASes and IXPs in geographical areas of influence. From this model, we extract central players of public peering such as hypergiant AS content providers and major regional traffic receivers. Most importantly, this graph model opens the way to apply spectral analysis using reduced Google matrix in order to retrieve the intensity of possible interactions between ASes on the basis of pure connectivity information. As an illustration, we retrieve the timely evolution of the peering network to show how the central content and cloud providers have increased their reach to eyeball networks during Covid-19 pandemic.
翻译:在互联网交换点(IXPs)进行大规模和不断增长的自动化系统水平交通交流。本文利用PDB数据库这一数据库,提供对IXPs系统ASes全球互联性的部分而合理的看法,以模拟一个复杂图解,从而能够对主要的互联网同侪播放者及其在一段时间内的互动进行定性。我们将PDB快照作为加权的双向双向双向图,称为PDB Craphy,以利用现有元数据捕捉IXPs拥有的港口规模ASes的港口规模。这个因特网的新模式展示了将ASes和IXPs组合在具有影响力的地理区域的复杂网络的相关特征。我们从这个模型中提取了一个公共同侪中央参与者,例如超大AS内容提供者和主要区域交通接收者。最重要的是,这个图形模型打开了利用谷歌缩小矩阵进行光谱分析的途径,以便在纯连通信息的基础上检索ASes之间可能进行的互动的强度。我们检索同侪网络的及时演变,以显示中央内容和云提供者如何在Covic-19大流行病期间扩大其对眼球网的覆盖范围。