What is the primary aim of autoscaling in cloud-native apps?

What is the primary aim of autoscaling in cloud-native apps?

• A. To scale based on the workload.
• B. To fix memory leaks.
• C. To limit the number of instances.
• D. To centralize configuration management.

Answer: (A)

Autoscaling is about adjusting the number of running instances in response to workload to keep performance targets while avoiding waste. In cloud-native apps, demand can spike or dip, so autoscaling monitors metrics like CPU usage, request rate, and queue length, and automatically increases or decreases the number of replicas. This ensures the system has enough capacity to handle traffic, maintaining latency and throughput, without overprovisioning resources. The other ideas aren’t the aim of autoscaling: fixing memory leaks is a debugging task, not something autoscaling does; limiting the number of instances would hinder the ability to handle load; and centralizing configuration management is about how you manage settings, not how you scale to demand.

Autoscaling is about adjusting the number of running instances in response to workload to keep performance targets while avoiding waste. In cloud-native apps, demand can spike or dip, so autoscaling monitors metrics like CPU usage, request rate, and queue length, and automatically increases or decreases the number of replicas. This ensures the system has enough capacity to handle traffic, maintaining latency and throughput, without overprovisioning resources.

The other ideas aren’t the aim of autoscaling: fixing memory leaks is a debugging task, not something autoscaling does; limiting the number of instances would hinder the ability to handle load; and centralizing configuration management is about how you manage settings, not how you scale to demand.