Which autoscaling method could use AI and Machine Learning to scale in anticipation of demand?

Which autoscaling method could use AI and Machine Learning to scale in anticipation of demand?

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

Answer: (A)

Anticipating demand with AI/ML is the essence of predictive autoscaling. By analyzing historical usage, patterns, seasonality, and trends, a predictive model forecasts future load and scales out before traffic arrives. This preemptive action helps keep latency low and avoids underprovisioning during spikes. Reactive autoscaling only reacts to current metrics after load increases, which can introduce delays and performance issues. Scheduled autoscaling operates at fixed times and doesn’t adapt to real-time patterns, making it less responsive to unexpected changes. The option that specifically uses AI/ML to predict and scale ahead of demand is predictive autoscaling.

Anticipating demand with AI/ML is the essence of predictive autoscaling. By analyzing historical usage, patterns, seasonality, and trends, a predictive model forecasts future load and scales out before traffic arrives. This preemptive action helps keep latency low and avoids underprovisioning during spikes. Reactive autoscaling only reacts to current metrics after load increases, which can introduce delays and performance issues. Scheduled autoscaling operates at fixed times and doesn’t adapt to real-time patterns, making it less responsive to unexpected changes. The option that specifically uses AI/ML to predict and scale ahead of demand is predictive autoscaling.