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On the geographic location of Internet resources

机译:关于Internet资源的地理位置

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One relatively unexplored question about the Internet's physical structure concerns the geographical location of its components: routers, links, and autonomous systems (ASes). We study this question using two large inventories of Internet routers and links, collected by different methods and about two years apart. We first map each router to its geographical location using two different state-of-the-art tools. We then study the relationship between router location and population density; between geographic distance and link density; and between the size and geographic extent of ASes. Our findings are consistent across the two datasets and both mapping methods. First, as expected, router density per person varies widely over different economic regions; however, in economically homogeneous regions, router density shows a strong superlinear relationship to population density. Second, the probability that two routers are directly connected is strongly dependent on distance; our data is consistent with a model in which a majority (up to 75%-95%) of link formation is based on geographical distance (as in the Waxman (1988) topology generation method). Finally, we find that ASes show high variability in geographic size, which is correlated with other measures of AS size (degree and number of interfaces). Among small to medium ASes, ASes show wide variability in their geographic dispersal; however, all ASes exceeding a certain threshold in size are maximally dispersed geographically. These findings have many implications for the next generation of topology generators, which we envisage as producing router-level graphs annotated with attributes such as link latencies, AS identifiers, and geographical locations.
机译:有关Internet物理结构的一个相对未开发的问题涉及其组件的地理位置:路由器,链接和自治系统(ASes)。我们使用两个大型Internet路由器和链接清单来研究此问题,这些清单通过不同的方法收集并且相隔大约两年。我们首先使用两个不同的最新工具将每个路由器映射到其地理位置。然后,我们研究路由器位置与人口密度之间的关系。在地理距离和链路密度之间;在AS的规模和地理范围之间。我们的发现在两个数据集和两种映射方法中都是一致的。首先,正如预期的那样,每个人在不同经济区域的路由器密度差异很大。但是,在经济上均一的地区,router刨机密度与种群密度显示出很强的超线性关系。其次,两个路由器直接连接的概率很大程度上取决于距离。我们的数据与一个模型一致,在该模型中,大部分(最多75%-95%)链接形成是基于地理距离的(如Waxman(1988)拓扑生成方法)。最后,我们发现AS的地理规模具有高度可变性,这与AS规模的其他度量(接口的程度和数量)相关。在中小型ASes中,ASes的地域分布差异很大。但是,所有超出特定阈值的AS都在地理位置上最大程度地分散了。这些发现对下一代拓扑生成器有很多影响,我们设想将其生成带有注释的路由器级图形,这些图形带有诸如链路等待时间,AS标识符和地理位置之类的属性。

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