突破边界与bdrmapIT:映射路由器所有权在互联网规模

Alexander Marder, M. Luckie, A. Dhamdhere, B. Huffaker, K. Claffy, Jonathan M. Smith
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引用次数: 58

摘要

最近出现了两种互补的从traceroute路径映射网络边界的方法[27,31]。这两种方法都应用启发式来告知从跟踪路由测量活动中提取的推论。bdrmap[27]使用来自特定网络的目标跟踪路由、别名解析探测技术和AS关系推断来推断该特定网络的边界以及附加在每个边界上的其他网络。MAPIT[31]解决了从许多不同网络发起的大量跟踪路由存档集合中推断所有as级网络边界的雄心勃勃的挑战。两者都是对最先进技术的重大贡献,并激发了合作,以探索结合这两种方法的潜力。我们提出并评估了bdrmapIT,这是探索的结果,它为互联网拓扑研究的这一持久和核心挑战提供了更完整、更准确和更通用的解决方案。bdrmapIT在两个互联网范围的跟踪路由数据集中映射AS边界时达到了91.8%-98.8%的精度,在不牺牲bdrmap映射单个网络的能力的情况下,极大地提高了map - it的覆盖范围。bdrmapIT源代码可从https://git.io/fAsI0获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pushing the Boundaries with bdrmapIT: Mapping Router Ownership at Internet Scale
Two complementary approaches to mapping network boundaries from traceroute paths recently emerged [27,31]. Both approaches apply heuristics to inform inferences extracted from traceroute measurement campaigns. bdrmap [27] used targeted traceroutes from a specific network, alias resolution probing techniques, and AS relationship inferences, to infer the boundaries of that specific network and the other networks attached at each boundary. MAPIT [31] tackled the ambitious challenge of inferring all AS-level network boundaries in a massive archived collection of traceroutes launched from many different networks. Both were substantial contributions to the state-of-the-art, and inspired a collaboration to explore the potential to combine the approaches. We present and evaluate bdrmapIT, the result of that exploration, which yielded a more complete, accurate, and general solution to this persistent and central challenge of Internet topology research. bdrmapIT achieves 91.8%-98.8% accuracy when mapping AS boundaries in two Internet-wide traceroute datasets, vastly improving on MAP-IT's coverage without sacrificing bdrmap's ability to map a single network. The bdrmapIT source code is available at https://git.io/fAsI0.
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