Deconstructing internet paths: an approach for AS-level detour route discovery

Detour paths provide overlay networks with improved performance and resilience. Finding good detour routes with methods that scale to millions of nodes is a challenging problem. We propose a novel approach for decentralised discovery of detour paths based on the observation that Internet paths that traverse overlapping sets of autonomous systems may benefit from the same detour nodes. We show how nodes can learn about overlap between Internet paths at the level of autonomous systems and demonstrate how they can exploit detours that other nodes have already found. Our approach is to cluster paths based on the extent to which the autonomous systems traversed overlap and gossip potential detours among nodes. We find that our centralised path clustering algorithm correctly classified over 90% of potential latency detours in a 176-node dataset drawn from PlanetLab. In our decentralised version, we detected 60% of potentially available detours with each node sampling data from only 10% of other nodes.