Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs
This addresses cloud platform instability for operators, but it appears incremental as it builds on existing RAG and LLM methods.
The paper tackles the problem of diagnosing and resolving cloud platform instability by introducing ARCA, a multi-modal RAG LLM system, which outperforms state-of-the-art alternatives in step-wise evaluations.
Today's cloud-hosted applications and services are complex systems, and a performance or functional instability can have dozens or hundreds of potential root causes. Our hypothesis is that by combining the pattern matching capabilities of modern AI tools with a natural multi-modal RAG LLM interface, problem identification and resolution can be simplified. ARCA is a new multi-modal RAG LLM system that targets this domain. Step-wise evaluations show that ARCA outperforms state-of-the-art alternatives.