Automatic Root Cause Analysis via Large Language Models for Cloud Incidents (2305.15778v4)

Abstract: Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the LLM for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years.

• The paper introduces RCACopilot, an innovative system that integrates large language models to automate root cause analysis for cloud incidents.
• The paper details a novel incident handler architecture that collects diagnostic data to predict causes and provide clear, contextual explanations.
• The paper demonstrates real-world efficacy with deployments at Microsoft that significantly reduce manual effort and improve incident resolution speed.

Understanding Cloud Incidents with RCACopilot

Introduction to RCACopilot

Cloud computing is a critical backbone for countless services relied upon daily by both individuals and businesses. With the proliferation of cloud infrastructure, maintaining uninterrupted and secure cloud operations has become increasingly crucial. Root cause analysis (RCA) plays a vital role in addressing incidents that affect cloud reliability and availability, but traditional manual approaches to RCA are time-intensive and prone to error. Enter RCACopilot—an innovative solution designed to automate RCA for cloud incidents using LLMs.

Automated Incident Handling

RCACopilot introduces an approach that revolutionizes how engineers handle cloud incidents. At its core, the system sports an "incident handler" for each type of cloud alert. These handlers are constructed using pre-set actions that collect diagnostic information relevant to the cloud incident. By automating this process, RCACopilot removes the manual burden from on-call engineers (OCEs), enabling them to focus their expertise on resolving incidents rather than navigating vast quantities of data.

The Integration of LLM

What sets RCACopilot apart is the integration of a LLM component that takes the collected diagnostic data to predict the root cause of an incident and provide explanatory narratives. This model is based on the prior understanding and patterns of incidents to present both the predicted cause and contextually relevant explanations. Such AI-driven insights drastically improve adaptability and scalability in incident response. Simultaneously, they bring down the amount of human intervention necessary to manage different types of incidents.

Real-World Efficacy

The real-world applicability of RCACopilot has been validated within Microsoft’s ecosystem, demonstrating its practical benefits. The system's diagnostic information collection component has been successfully utilized at Microsoft for over four years, and its root cause prediction component has seen deployment within an incident management team at Microsoft for several months.

CC Concepts and Keywords

RCACopilot falls under key computer systems organization categories such as cloud computing, as well as software engineering constructs relating to maintaining software. Its operational methodology leverages the capabilities of LLMs for tasks like automatic summarization, prediction, and categorization in the context of cloud systems.

Conclusion

With the introduction of RCACopilot, cloud service providers now have access to a system that not only enhances the reliability and efficiency of RCA but also aligns with the evolving nature of cloud services. Its contributions extend from offering an end-to-end automated solution for cloud incident RCA to presenting real-world success stories in major cloud systems. This exemplifies how integrating innovative AI techniques with domain-specific knowledge can be transformative in managing the complex landscape of cloud incidents.