A first step toward understanding inter-domain routing dynamics
A first step toward understanding inter-domain routing dynamics
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DOI：
10.1145/1080173.1080187
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发表时间：
2005-08
期刊：
影响因子：
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通讯作者：
Kuai Xu;J. Chandrashekar;Zhi-Li Zhang
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文献类型：
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作者：
Kuai Xu;J. Chandrashekar;Zhi-Li Zhang
BGP 更新由各种事件触发，例如链路故障、重置、路由器崩溃、配置更改等。了解这些更新并识别潜在事件是调试和排除 BGP 路由问题的关键。在本文中，作为解决 BGP 更新根本原因分析这一更困难问题的第一步，我们讨论是否以及如何分离由不同底层事件触发的更新。具体来说，我们探索使用PCA（主成分分析）这种众所周知的统计多变量技术来实现这一目标。我们提出了一种基于PCA的方法，从BGP更新流中获取一组簇；其中每一个都是受同一基础事件影响的一组实体（前缀或 AS）。然后我们使用模拟获得的 BGP 数据演示了我们的方法，并表明该方法非常有效。此外，我们还对包含众所周知的大规模事件的 BGP 数据进行高级分析。
BGP updates are triggered by a variety of events such as link failures, resets, routers crashing, configuration changes, and so on. Making sense of these updates and identifying the underlying events is key to debugging and troubleshooting BGP routing problems. In this paper, as a first step toward the much harder problem of root cause analysis of BGP updates, we discuss if, and how, updates triggered by distinct underlying events can be separated. Specifically, we explore using PCA (Principal Components Analysis), a well known statistical multi-variate technique, to achieve this goal.We propose a method based on PCA to obtain a set of clusters from a BGP update stream; each of these is a set of entities (either prefixes or ASes) which are affected by the same underlying event. Then we demonstrate our approach using BGP data obtained by simulations and show that the method is quite effective. In addition, we perform a high level analysis of BGP data containing well known, large scale events.