Google has published details of its internal fleet-wide A/B experimentation system, designed to standardize experiment assignment, exposure logging, and configuration propagation across its distributed service infrastructure. The system uses a centralized framework with a unified assignment layer that supports hierarchical traffic allocation, reducing conflicts between overlapping experiments. Deterministic user bucketing ensures stable exposure over time, while exposure logging distinguishes assigned from truly exposed populations for more reliable metric analysis. Experiment configurations are distributed locally to serving systems to minimize runtime latency. Analytics pipelines aggregate results across services, enabling end-to-end impact measurement and faster product iteration at scale.
