Which autoscaling method uses predictive analytics to forecast demand and scale ahead of time?

Which autoscaling method uses predictive analytics to forecast demand and scale ahead of time?

• A. Reactive Autoscaling
• B. Scheduled Autoscaling
• C. Predictive Autoscaling
• D. Auto Autoscaling

Answer: (C)

Predictive autoscaling uses forecasting to anticipate future load and adjust the number of running instances before demand rises. This proactive approach relies on historical data, patterns, and possibly external signals to predict metrics like CPU usage, request rate, or queue length, so capacity can be increased ahead of time to meet expected traffic and then reduced when the forecasted demand drops. This helps maintain performance and can optimize costs by avoiding both under-provisioning during spikes and over-provisioning during lulls. In contrast, reactive autoscaling waits for current metrics to exceed thresholds, which can introduce latency, and scheduled autoscaling changes capacity at set times regardless of actual demand.

Predictive autoscaling uses forecasting to anticipate future load and adjust the number of running instances before demand rises. This proactive approach relies on historical data, patterns, and possibly external signals to predict metrics like CPU usage, request rate, or queue length, so capacity can be increased ahead of time to meet expected traffic and then reduced when the forecasted demand drops. This helps maintain performance and can optimize costs by avoiding both under-provisioning during spikes and over-provisioning during lulls. In contrast, reactive autoscaling waits for current metrics to exceed thresholds, which can introduce latency, and scheduled autoscaling changes capacity at set times regardless of actual demand.