Improved Topology Extraction Using Discriminative Parameter Mining of Logs

Analytics on log data from various sources like application, middleware and infrastructure plays a very important role in troubleshooting of distributed applications. The existing tools for log analytics work by mining log templates and template sequences. The template sequences are then used to derive the application control flow or topology. In this work, we show how the use of parameters in logs enables discovery of a more accurate application topology, thereby aiding troubleshooting. An accurate application topology information helps in better correlation of logs at runtime, enabling troubleshooting tasks like anomaly detection, fault localization and root cause analysis to be more accurate.