Autoscaling is an essential tool for modern applications that need to dynamically adjust their resources based on demand, thereby optimizing performance and costs. This technique allows organizations to handle variations in traffic without manual intervention, ensuring that services are always available and efficient.
Below I share with you some key practices for implementing autoscalling, based on some practical cases that we have recently encountered and that you can read below:
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1. Choose the right scaling strategy
There are two main ways to implement autoscaling:
Vertical Scaling , which increases or decreases the instance size. This method can be more disruptive as it often requires downtime, making it less suitable for real-time adjustments.
Horizontal scaling , which adds or removes instances as needed. This method allows the system to handle increases in workload without interruption and is ideal for applications built in the cloud due to its flexibility and lower impact on user experience.
2. Use relevant metrics and thresholds
Successful autoscaling depends on monitoring the right metrics, such as CPU, memory, and response time usage. These metrics should align with the specific needs of your application. For example, for CPU-intensive applications, CPU usage is the primary metric, while for applications that rely on background processing, queue length is more relevant. Setting appropriate thresholds is key to avoiding rapid and constant scaling actions, known as “flapping,” which can cause instability and increase costs.
3. Implement a cooling period
After scaling, applications typically need time to stabilize. A cool-down period prevents new scaling actions from being triggered immediately, allowing the system to absorb the added capacity without overreacting to transient spikes in demand. Cool-down periods typically range from 5 to 10 minutes, but this can vary depending on your application's startup time and specific needs.
4. Optimize costs with spot instances and scheduled scaling
For cost-sensitive applications, using Spot Instances can significantly reduce expenses by utilizing spare capacity in the cloud at reduced rates. However, these instances can be terminated with little notice, making them ideal for use alongside On-Demand Instances in a flexible configuration that maintains reliability. Additionally, scheduled scaling allows you to anticipate peak times, such as during business hours, and proactively allocate resources, helping to manage predictable traffic without over-provisioning.