Causal analysis of network logs with layered protocols and topology knowledge

To detect root causes of failures in large-scale networks, we need to extract contextual information from operational data automatically. Correlation-based methods are widely used for this purpose, but they have a problem of spurious correlation, which buries truly important information. In this work, we propose a method for extracting contextual information in network logs by combining a graph-based causal inference algorithm and a pruning method based on domain knowledge (i.e., network protocols and topologies). Applying the proposed method to a set of log data collected from a nation-wide R & E network, we demonstrate that the pruning method reduced processing time by 74% compared with a single-handed causal analysis method, and it detected more useful information for troubleshooting compared with an existing area-based method.