Using the Gini Coefficient to Plan Edge Capacity

Fastly's capacity planning model centers on the Gini coefficient, a metric traditionally used in economics to measure inequality, which surprisingly proved effective in predicting traffic behavior during major events. This approach emerged after standard AI and ML techniques failed to accurately forecast capacity demands on unpredictable, high-traffic days. The model treats traffic inequality as a critical signal, linking it to cache behavior and CPU utilization to determine headroom for Fastly's Points of Presence (POPs). By focusing on the concentration of workloads, the model optimizes cache efficiency, allowing for more accurate scenario analysis and capacity planning. The simplicity of this model, utilizing robust regression techniques, enables Fastly to address diverse traffic challenges, leading to more efficient infrastructure investment decisions and traffic management strategies.