Using the Gini Coefficient to Plan Edge Capacity
4 hours ago
- #cache-optimization
- #capacity-planning
- #edge-computing
- Fastly uses the Gini coefficient, an economic metric for inequality, as a key signal in its capacity planning model to measure traffic inequality.
- Traffic concentration, especially during events like major game releases or live streaming, significantly impacts cache hit ratios and CPU utilization, which generic AI/ML models often failed to predict accurately.
- The capacity model predicts cache hit ratios based on rescaled Gini coefficient and top customer traffic fractions, which then informs CPU utilization forecasts through a simple, interpretable regression model.
- Fastly's approach supports scenario-based analysis for headroom calculations, allowing planners to evaluate capacity under different traffic mixes, such as uniform growth, compute-heavy workloads, or specific customer events.
- The model has been in production for over a year, enabling improved traffic placement decisions by intentionally concentrating compatible workloads to enhance cache efficiency and performance on older hardware.