"For better or worse, benchmarks shape a field." FD.io's approach to the better: open, reproducible benchmarks-as-code that guide development and guard against regressions in the VPP data plane, via CSIT. Problem: a race-track number doesn't translate to production deployments. CSIT's approach: MLRsearch (IETF BMWG, RFC in publication) for conditional throughput (NDR/PDR) with explicit stopping rules and inspectable artifacts; a continuous open-source benchmarking pipeline spanning packets and sessions; and a test matrix covering IMIX, QUIC/TLS, NAT, IPsec, ACL, SRv6, and NGFW/proxy use cases. This methodology drives terabit-class packet and session performance on commodity x86/Arm - reliably and repeatably. Takeaways: a replicable recipe (tools, configs, artifacts) for your lab; why benchmarks-as-code beat ad-hoc testing; and concrete contribution paths across CSIT and VPP (tests, profiles, analysers, data visualisation).

Relevant links: https://fd.io/ https://csit.fd.io/ https://wiki.fd.io/ https://github.com/FDio/