CloudAutoScaling

CloudAutoScaling is a cloud computing capability that automatically adjusts the provisioning of computing resources in response to observed demand. It aims to maintain application performance while optimizing cost by scaling resources up or down as traffic and workload vary. Resources that can be scaled include virtual machines, containers, and related compute quotas, as well as associated storage and network capacity.

The system typically relies on continuous monitoring of metrics such as CPU utilization, memory usage, request rate, queue depth, and custom application metrics. A policy engine evaluates these signals against predefined rules or targets and issues scaling actions, such as adding or removing instances, or increasing or decreasing container replicas. Safety mechanisms like health checks, cooldown periods, and dependency awareness help prevent unstable oscillations and ensure that scaling actions do not disrupt service availability.

CloudAutoScaling supports multiple modes of scaling. Horizontal scaling adds or removes independent units of compute (for example, increasing the number of instances or pods). Vertical scaling adjusts the resource size of existing units. Scheduled scaling allows predefined changes at specific times to accommodate predictable patterns. Policies commonly used include target tracking scaling, which maintains a chosen metric level; simple or step scaling, which triggers actions at defined thresholds; and predictive scaling, which leverages historical data to anticipate demand.

Key considerations include setting appropriate minimum and maximum bounds, ensuring stateless versus stateful service compatibility, managing data persistence, and understanding potential latency in scaling actions. Widely used examples include AWS Auto Scaling, Google Cloud Autoscaler, and Kubernetes Horizontal Pod Autoscaler.