SoftNER: Mining Knowledge Graphs From Cloud Incidents

Abstract

Incident management is a critical part of the DevOps processes for developing and operating large-scale services in the cloud. Incident reports filed by customers are largely unstructured making any automated diagnosis or mitigation non-trivial. It requires on-call engineers to parse verbose reports to understand the issue and locate key information. Prior work has looked into extraction of key attributes or entities like error codes, tenant Ids, stack traces, etc. from incident and bug reports. Although a flat list of entities is informative, to unlock the full potential of knowledge extraction, it is necessary to provide context to these entities. For instance, the relations between the real-world concepts or objects that these entities represent in otherwise unstructured data is useful for downstream tasks like incident linking, triaging and mitigation. With this additional context, entities are transformed from "Strings" to "Things". In this work, we present an approach to mine and score binary entity relations from co-occurring entity pairs. We evaluate binary relations extracted and show that our approach has a high precision of 0.9. Further, we construct knowledge graphs automatically and show that the implicit knowledge in the graph can be used to mine and rank relevant entities for distinct incidents, by mapping entities to clusters of incident titles.

AutoTSG: Learning and Synthesis for Incident Troubleshooting

“…Recent work has focused on multiple aspects of incident management like triaging [9,10], mitigation [24], diagnosis [6,39,51], and more. Particularly close to our work, are efforts that attempt to mine structured knowledge from various artifacts, such as incident reports [26,59,60] and root cause documentation [57]. However, we tackle an aspect of incident management, that has received relatively lesser attention -TSGs.…”