TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetes
Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators cannot send production logs to a hosted model at all. On-premise inference removes the second constraint but raises a question live-cluster benchmarks have not addressed: when one workstation GPU fixes both the model and the context budget, how should evidence be retrieved, and what happens when the runbooks the model consults have been tampered with? We present TriFleetRCA, a pipeline running entirely on one on-premise GPU that collects evidence at one of three scopes (pod, namespace, cluster), ranks it by template de-duplication then BM25, ...
| Authors | Rohit Patel, Susil Kumar Mohanty, Jeenal Chaudhary |
|---|---|
| Primary category | cs.CR |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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