Scaling Policies

Whether our application requires more power because of a surge in users or less because it is a quiet day, the scaling policies ensure that our application gets only what it requires for optimal running.

Types of scaling policies

AWS offers several types of Scaling Policies, each suited to different scenarios:

• Manual scaling: Directly adjust the number of instances manually as needed.

• Dynamic scaling: Automatically adjust resources in real-time based on demand. This includes:

  • Target tracking: This type of scaling policy targets the value of a metric, and it can be targeted to a constant value or a variable level of performance.

  • Step scaling: This allows users to scale in finer granularity the number of resources concerning the size of the metric breach. It is applicable for applications that exhibit variable load patterns.

  • Simple scaling: It responds to CloudWatch alarms by adjusting the number of instances linearly, based on the rules defined. It waits for a cooldown period after each scaling activity, which can delay further scaling actions, making it suitable for environments with steady and predictable workload changes.

  • Scaling based on SQS: It scales based on the messages in the SQS queue to manage workload.

• Scheduled scaling: Predicting times when our application will either have high or low traffic, maybe because of a sale or weekend hours, can now be possible with the help of scheduled scaling. Scheduled Scaling will help determine the amount of resources to change based on the alert.

• Predictive scaling: Utilizes machine learning to analyze historical data and predict future demands, automatically adjusting resources to efficiently meet predicted loads and optimizing performance and cost.

How scaling policies work

Scaling Policies are connected to monitoring tools, like AWS CloudWatch, which will monitor different metrics about how well our application performs. From this, we can take actions to scale up or down our resources according to our Scaling Policies. Below is the brief process of scaling:

• Set the criteria: First, clear conditions under which our resources are supposed to scale will be defined. This could be CPU usage, response times, or even the number of requests per second.

• Select the action: Next comes what action should be taken when the conditions are met. Add more instances if the load is increasing or scale down for cost-saving.

• Continuous monitoring: With our policies in place, AWS is always looking at our application, ready to scale resources up or down, as needed.

Metrics used in scaling policies

Scaling decisions are often based on key performance indicators such as CPU utilization, network I/O, and response times. These metrics are monitored through tools like AWS CloudWatch, which provides the data necessary to trigger scaling actions.

Why scaling policies matter

Scaling policies give us an awesome mix of effectiveness, economy, and performance optimization. The automated process of scaling ensures that our applications are ever ready for the demand of users without spending money on resources that may never be used. Further, scaling policies enable our applications to respond quickly in all load forms to optimize user experience.