On the dynamics of interdomain routing in the Internet

The routes used in the Internet's interdomain routing system are a rich information source that could be exploited to answer a wide range of questions.  However, analyzing routes is difficult, because the fundamental object of study is a set of paths. In this dissertation, we present new analysis tools -- metrics and methods -- for analyzing paths, and apply them to study interdomain routing in the Internet over long periods of time. Our contributions are threefold. First, we build on an existing metric (Routing State Distance) to define a new metric that allows us to measure the similarity between two prefixes with respect to the state of the global routing system. Applying this metric over time yields a measure of how the set of paths to each prefix varies at a given timescale. Second, we present PathMiner, a system to extract large scale routing events from background noise and identify the AS (Autonomous System) or AS-link most likely responsible for the event. PathMiner is distinguished from previous work in its ability to identify and analyze large-scale events that may re-occur many times over long timescales. We show that it is scalable, being able to extract significant events from multiple years of routing data at a daily granularity. Finally, we equip Routing State Distance with a new set of tools for identifying and characterizing unusually-routed ASes. At the micro level, we use our tools to identify clusters of ASes that have the most unusual routing at each time. We also show that analysis of individual ASes can expose business and engineering strategies of the organizations owning the ASes.  These strategies are often related to content delivery or service replication. At the macro level, we show that the set of ASes with the most unusual routing defines discernible and interpretable phases of the Internet's evolution. Furthermore, we show that our tools can be used to provide a quantitative measure of the "flattening" of the Internet