Disaggregated Scheduled Fabric: Scaling Meta’s AI Journey

What this post added

This post introduces Disaggregated Scheduled Fabric (DSF), Meta's next-generation network fabric technology designed for AI training networks. It details the challenges with traditional IP fabrics for AI workloads (elephant flows, low entropy, suboptimal utilization) and how DSF addresses them through a disaggregated architecture (interface nodes and fabric nodes), packet spraying, and a credit-based congestion control algorithm with Virtual Output Queuing (VOQ). The post also describes the scaling of DSF from single AI zones to dual-stage fabrics (L2 zones) and regional interconnectivity (L3 super-spine), highlighting the use of FBOSS and OCP-SAI. A key feature, Input Balanced Mode, is explained as a mechanism to prevent congestion during link failures by dynamically adjusting traffic based on reduced reachability.