Extracting Network-Wide Correlated Changes From Longitudinal Configuration Data
Source: Purdue University
IP network operators face the challenge of making and managing router configuration changes to serve rapidly evolving user and organizational needs. Changes are expressed in low-level languages, and often impact multiple parts of a configuration file and multiple routers. These dependencies make configuration changes difficult for operators to reason about, detect problems in, and troubleshoot. In this paper, the authors present a methodology to extract network-wide correlations of changes. From longitudinal snapshots of low-level router configuration data, their methodology identifies syntactic configuration blocks that changed, applies data mining techniques to extract correlated changes, and highlights changes of interest via operator feedback. Employing their methodology, they analyze an 11-month archive of router configuration data from 5 different large-scale enterprise Virtual Private Networks (VPNs).