Autoscaling

Autoscaling is the automatic adjustment of computing resources for an application or service in response to varying demand. It monitors metrics and executes predefined policies to maintain performance while controlling costs. Autoscaling can be configured to respond to traffic, workload, or utilization changes, reducing manual intervention.

The two primary forms are horizontal scaling and vertical scaling. Horizontal scaling adds or removes individual compute units, such as virtual machines or containers, effectively increasing or decreasing the number of instances. Vertical scaling increases or decreases the capacity of existing machines by adjusting resource limits like CPU or memory. Horizontal scaling is more common in cloud and container environments, while vertical scaling is simpler but has practical limits and potential downtime.

Triggers and policies guide autoscaling. Common triggers include threshold-based rules (for example, scale out if average CPU usage exceeds a threshold), scheduled or time-based scaling, and predictive scaling based on historical data. Policies also specify minimum and maximum counts, cooldown periods to avoid rapid oscillations, and how to handle partial or delayed metrics.

Implementation typically involves a control plane that collects metrics, evaluates policies, and provisions or deprovisions resources through the cloud provider or container orchestrator. Examples include cloud auto-scaling groups and managed services, as well as Kubernetes components such as Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler, which manage pods and nodes respectively.

Considerations include latency between metric changes and actions, suitability for stateless versus stateful workloads, data consistency, and dependencies between services. Costs, reliability, and the quality of monitoring data influence design choices and effectiveness.

Common use cases involve web front ends, microservices, data processing pipelines, and batch jobs with fluctuating workloads, where autoscaling helps maintain service levels and optimize resource utilization.