AI-Powered Performance Insights: Integrating OVS/OVN automatic performance regression analysis with LLMs#

Joe Talerico (Red Hat), Jose Castillo Lema (Red Hat), Mohit Sheth (Red Hat)

• Why: Shows how performance engineering for Kubernetes networking is being automated and made LLM-queryable — shift-left performance testing operating at real scale, with coverage numbers rather than claims.
• What: Red Hat Perf&Scale's continuous performance testing: benchmarks (k8s-netperf, ingress-perf, cluster-density-v2) run against PRs early; Orion, a Python library doing automated statistical regression detection (Apache Otava e-divisive change-point detection) over an Elastic/OpenSearch data warehouse; and Orion-MCP, an MCP server that lets LLMs answer "has my PR regressed OpenShift?", correlate regressions with PR changelogs, and surface version-tagged results (OVN, OVN-K, OVS versions per run). Scale: 5 platforms (AWS, Azure, GCP, IBM, bare metal, plus ROSA/ARO), 100+ weekly tests; 2025 added net-new workloads for UDN L2/L3, Virt UDN, NetworkPolicy, EgressIP and BGP, with 30+ downstream bugs filed.
• How: Fingerprint-based result retrieval (platform, node types/counts, benchmark, networkType: OVNKubernetes), change-point detection on the matched series, and MCP tool calls surfaced through Slack (GitHub integration next). Next steps: agentic AI for CPT and pushing a perf workload into ovn-ci upstream.
• Where applicable: OpenShift/OVN-Kubernetes development workflow; Red Hat-internal tooling, but the Orion + Orion-MCP pattern is directly replicable by any team with a benchmark data warehouse.