Automating Root Cause Analysis in OVS/OVN-Based Deployments Using AI/ML#

Speakers: Gurpreet Singh, Red Hat, Inc.

Why: A PM-to-PM talk that is thin on its own results but signals where vendor investment is going: closed-loop, ML-driven RCA for virtualized networking.

What: The problem statement is real and well-framed: OVS/OVN-level metrics are missing in shared virtualized environments, nothing correlates metrics to policies or resource allocation, and RCA is manual and slow. The proposed approach: Self-Organizing-Map-based anomaly detection (citing prior published work reporting 99% accuracy on real DC data, with acknowledged processing-time and scale challenges), a fault-injection sandbox (CPU/network/memory stress, Tx/Rx queue oversubscription) for pre-training models on simulated environments, OpenTelemetry-based collection, and closed-loop anomaly mitigation.

How: Architecture and prior work only — no results from the authors' own deployment in the available material.

Where applicable: OpenStack/OCP-style virtualized infrastructure; watch with skepticism, but the "train on injected faults in a sandbox before production" pattern is a credible approach to the data-scarcity problem in network ML.