抄録
Predicting the routing paths between any given pair of Autonomous Systems (ASes) is very useful in network diagnosis, traffic engineering, and protocol analysis. Existing methods address this problem by resolving the best path with a snapshot of BGP (Border Gateway Protocol) routing tables. However, due to route deficiencies, routing policy changes, and other causes, the best path changes over time. Consequently, existing methods for path prediction fail to capture route dynamics. To predict AS-level paths in dynamic scenarios (e.g. network failures), we propose a per-neighbor path ranking model based on how long the paths have been used, and apply this routing model to extract each AS's route choice configurations for the paths observed in BGP data. With route choice configurations to multiple paths, we are able to predict the path in case of multiple network scenarios. We further build the model with strict policies to ensure our model's routing convergence; formally prove that it converges; and discuss the path prediction capturing routing dynamics by disabling links. By evaluating the consistency between our model's routing and the actually observed paths, we show that our model outperforms the state-of-the-art work [4].
