Matryoshka: Realizing Hyperscale Data Center Network Design for the AI Era

Over the past decade, data center networking (DCN) has undergone substantial transformation in terms of both scale and complexity. Developing a DCN entails multiple intricate steps, such as establishing physical connections, configuring logical network addressing, and defining high-level routing policies. While extensive work has focused on logical DCN design and physical deployment, a critical gap remains: materializing these designs into concrete switch configurations-a necessary step to realize the development procedure. This problem is especially acute in the AI era, as hyperscale, rapidly evolving, and highly heterogeneous AI-driven clusters place unprecedented demands on DCN design and implementation.

This paper presents Matryoshka, Meta's production-scale DCN design system that bridges this gap. Matryoshka employs an intent-based, model-driven approach to systematically compile high-level DCN design intents into working switch configurations. Operational for over six years, Matryoshka has supported orders-of-magnitude growth in Meta's DCN infrastructure, guiding the design nearly 900 DCNs across 18 distinct types, including the latest 100K-GPU supercluster for AI training. We share our experience in building and operating Matryoshka, highlighting how it empowers the rapid design and evolution of AI clusters nowadays.