AI

An Active Approach to Measuring Routing Dynamics Induced by Autonomous Systems

Abstract

We present an active measurement study of the routing dynamics induced by AS-path prepending, a common method for controlling the inbound traffic of a multi-homed ISP. Unlike other inter-domain inbound traffic engineering methods, AS-path prepending not only provides network resilience but does not increase routing table size. Unfortunately, ISPs often perform prepending on a trail-and-error basis, which can lead to suboptimal results and to a large amount of network churn. We study these effects by actively injecting prepended routes into the Internet routing system using the RIPE NCC RIS route collectors and observing the resulting changes from almost 200 publicly-accessible sources of BGP information. Our results show that our prepending methods are simple and effective and that a small number of ASes is often responsible for large amounts of the route changes caused by prepending. Furthermore, we show that our methods are able to reveal hidden prepending policies to prepending and tie-breaking decisions made by ASes; this is useful for further predicting the behavior of prepending.