Auto Scaling

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As your business grows and your service gains more popularity, you’ll start experiencing sudden increases in traffic. While it’s good to see your service doing well, this sudden influx of traffic can overwhelm your servers, causing them to crash or slow down, which leads to a frustrating user experience. Auto scaling is a solution that can help you handle sudden traffic spikes by automatically increasing or decreasing the number of running instances based on some metric. In this article, we’ll discuss the importance of auto scaling and some tips and tricks to make it work efficiently.

Auto scaling allows you to handle sudden traffic spikes without any manual intervention. When traffic suddenly increases, the auto-scaling feature will automatically increase the number of running instances to handle the traffic. This ensures that your service is always available, even during periods of high traffic.

Auto scaling eliminates the need to set up alerts for normal traffic increases. This saves your on-call members from being unnecessarily alerted during periods of normal traffic. Instead, they can focus on critical alerts that require immediate attention.

Auto scaling can help you save money by automatically scaling down your service when there is low traffic. This ensures that you’re only paying for the resources you need, which makes your service more cost-efficient.

Before you start using auto scaling, it’s important to perform capacity planning. This involves determining the right scaling configuration based on your expected traffic. By doing this, you’ll be able to set up your auto scaling feature to handle sudden traffic spikes more efficiently.

While auto scaling is designed to handle sudden traffic spikes, there is a delay in the process. If you can predict a sudden increase in traffic in advance, it’s best to manually scale your service up right before the spike occurs. This ensures that your service is ready to handle the increased traffic.

It’s important to measure how long it takes to scale your service up, how much traffic each instance of your service should handle, and how much traffic you expect in normal operation. Based on this data, you can estimate the point at which you should trigger a scaling event.

It’s important to review and modify your scaling values over time to strike a good balance between stability and cost efficiency. This ensures that your service is always available while minimizing your costs.

Ideally, your average CPU usage should always be the Horizontal Pod Autoscaler (HPA) target multiplied by the CPU request. For example, if your CPU request is 500m and the HPA target is 65%, then your CPU usage should always be around 325m.

For simple services that follow the resource request/limit guidelines, good HPA targets are unlikely to be outside the range of 50% to 75%. A good starting point is 65% (i.e. ~2/3).

Auto scaling is an important feature that allows you to handle sudden traffic spikes without any manual intervention. By following the tips and tricks mentioned in this article, you can ensure that your auto scaling feature is set up efficiently and cost-effectively.