Throughput autoscaling: Dynamic sizing for Facebook.com

What this post added

Introduced throughput autoscaling for Facebook.com's web tier, moving from static sizing to dynamic capacity adjustments based on estimated workload and disaster scenarios. This system uses ML models to predict steady-state and disaster demand, and load testing to determine throughput supply, leading to improved resource utilization and cost savings. The approach models demand in terms of throughput metrics (e.g., RPS) rather than utilization metrics, allowing for more accurate capacity planning even for unobserved workloads.