Inferring Catchment in Internet Routing

Abstract

BGP is the de-facto Internet routing protocol for exchanging prefix reachability information between Autonomous Systems (AS). It is a dynamic, distributed, path-vector protocol that enables rich expressions of network policies (typically treated as secrets). In this regime, where complexity is interwoven with information hiding, answering questions such as "what is the expected catchment of the anycast sites of a content provider on the AS-level, if new sites are deployed?", or "how will load-balancing behave if an ISP changes its routing policy for a prefix?", is a hard challenge. In this work, we present a formal model and methodology that takes into account policy-based routing and topological properties of the Internet graph, to predict the routing behavior of networks. We design algorithms that provide new capabilities for informative route inference (e.g., isolating the effect of randomness that is present in prior simulation-based approaches). We analyze the properties of these inference algorithms, and evaluate them using publicly available routing datasets and real-world experiments. The proposed framework can be useful in a number of applications: measurements, traffic engineering, network planning, Internet routing models, etc. As a use case, we study the problem of selecting a set of measurement vantage points to maximize route inference. Our methodology is general and can capture standard valley-free routing, as well as more complex topological and routing setups appearing in practice.