X-Lifecycle Learning for Cloud Incident Management using LLMs

This is the authors' abstract. We don't add key points for this paper.

Incident management for large cloud services is a complex and tedious process that requires a significant amount of manual effort from on-call engineers (OCEs). OCEs typically leverage data from different stages of the software development lifecycle [SDLC] (e.g., codes, configuration, monitor data, service properties, service dependencies, trouble-shooting documents, etc.) to generate insights for detection, root cause analysis and mitigation of incidents. Recent advancements in large language models [LLMs] (e.g., ChatGPT, GPT-4, Gemini) have created opportunities to automatically generate contextual recommendations for the OCEs, assisting them in quickly identifying and mitigating critical issues. However, existing research typically takes a silo-ed view of solving a certain task in incident management by leveraging data from a single stage of the SDLC. In this paper, we demonstrate that augmenting additional contextual data from different stages of the SDLC improves the performance of two critically important and practically challenging tasks: (1) automatically generating root cause recommendations for dependency failure related incidents, and (2) identifying the ontology of service monitors used for automatically detecting incidents. By leveraging a dataset of 353 incidents and 260 monitors from Microsoft, we demonstrate that augmenting contextual information from different stages of the SDLC improves the performance over state-of-the-art methods.