Autoscaling is a technique used to automatically adjust the capacity of a system in response to changing demand. This can be used to ensure that a system always has enough resources to handle the workload, while at the same time minimizing the cost of those resources when they are not needed.
There are several key components to an autoscaling system:
- Scaling policies: These define the rules that the autoscaling system uses to determine when to scale up or scale down. For example, a policy might specify that the system should scale up if the average CPU utilization exceeds 70% for a period of time, or scale down if the average CPU utilization falls below 30% for a period of time.
- Scaling triggers: These are events that cause the autoscaling system to evaluate the scaling policies. Triggers can be based on a variety of factors, such as changes in the workload, changes in the cost of resources, or even external events like the time of day.
- Scaling actions: These are the actions that the autoscaling system takes in response to a scaling trigger. Common actions include adding or removing resources, such as increasing the number of instances in an auto-scaled group, or modifying the configuration of existing resources, such as increasing the size of a database.
- Scaling optimizers: These are algorithms that help the autoscaling system make more intelligent decisions about when and how to scale. For example, an optimizer might consider the current and projected workload, the cost of different types of resources, and the availability of those resources when deciding whether to scale up or down.
Overall, the goal of autoscaling is to ensure that a system has the right amount of resources to meet the demands placed on it, while minimizing the cost of those resources when they are not needed. By using autoscaling, organizations can improve the performance and reliability of their systems, while also reducing the costs associated with running them.