AutoScalingPolicies

AutoScalingPolicies are a set of rules used by an autoscaling system to automatically adjust the number of running instances in response to changing demand. They define when to scale out (add capacity) or scale in (remove capacity), and by how much, based on monitored metrics such as CPU usage, request latency, queue length, or custom application metrics. Policies are evaluated at regular intervals and are constrained by configured minimum and maximum instance counts. A cooldown period may be used to prevent rapid successive changes and to stabilize fluctuations, allowing the system to observe the impact of a change.

Policy types commonly supported include: target tracking scaling policies, which aim to keep a chosen metric near a target value; simple scaling policies, which trigger a fixed adjustment when a threshold is crossed; step scaling policies, which apply varying adjustment amounts depending on how far the metric deviates from the threshold; and scheduled scaling policies, which adjust capacity at predefined times or days.

In practice, AutoScalingPolicies are associated with an autoscaling group or equivalent construct in cloud or container platforms. Examples include AWS Auto Scaling Groups, which support target tracking, simple, step, and scheduled policies; Google Cloud's Autoscaler and Kubernetes Horizontal Pod Autoscaler, which implement similar concepts through metrics and scaling rules.

Key considerations for effective policies include selecting meaningful metrics, avoiding overly aggressive scaling tied to short-term spikes, properly tuning cooldown periods, and ensuring safe limits with min/max capacity. Regular testing and monitoring help prevent oscillations and ensure cost efficiency while meeting performance targets.