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HomeOperationsSaving on cloud costs with Kubernetes autoscaling

Saving on cloud costs with Kubernetes autoscaling

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Looking to save on cloud costs? Kubernetes autoscaling can help! By automatically scaling your Kubernetes cluster up or down based on demand, you can avoid paying for unused resources. This can be a great way to save money, especially for businesses with fluctuating demand.

Kubernetes autoscaling is easy to set up and can be customized to fit your needs. For example, you can specify the minimum and the maximum number of nodes in your cluster and the circumstances under which the cluster should scale up or down. 

You can also set up autoscaling to scale based on specific metrics, such as CPU usage. If you’re not already using Kubernetes, it’s easy to start. Many cloud providers offer managed Kubernetes services, such as Amazon EKS and Google Kubernetes Engine, which make it easy to set up and manage a Kubernetes cluster without worrying about the underlying infrastructure.

What is Kubernetes autoscaling?

It’s the process of automatically scaling your application or workload in response to changes in demand. This can be done in response to traffic levels, CPU utilization, or other factors. Autoscaling ensures that your application always has the resources to meet demand without manual intervention.

If the average CPU utilization of a deployment or replica set is too low, the number of pods is increased. If the average CPU utilization is too high, the number of pods is decreased. Kubernetes autoscaling is a great way to ensure that your deployments and replica sets always have the right pods. This can save you time and money by reducing the need to scale your deployments manually. It is important to understand the types of autoscaling available and how they can be used to meet your specific needs.

Kubernetes offers three types of autoscaling: vertical scaling, horizontal scaling, and cluster scaling:

a. Horizontal pod autoscaling: Automatically scales by adding or removing instances (nodes) from the application. Horizontal autoscaling is usually more efficient than vertical autoscaling because it can scale individual components of the application. Horizontal autoscaling is the most common type of autoscaling. 

b. Vertical pod autoscaling: Automatically scales by changing the resources (CPU, memory, etc.) allocated to each instance. Vertical autoscaling is less common but can be more effective in some situations. It can be more efficient than horizontal autoscaling in cases where the application could be better suited to horizontal scaling.

c. Cluster Autoscaling: Automatically scales the number of nodes in the cluster.

Best practices for Kubernetes autoscaling

a. Reduce costs with mixed instances

One way to reduce costs is to use mixed instances for Kubernetes autoscaling. This means you can use a mix of on-demand and reserved instances, saving you money. To do this, you must set up your autoscaling group to use a mix of on-demand, reserved, and spot instances. Then, when you scale up your cluster, Kubernetes will automatically use the reserved instances first. This can save you money, as you’re not paying for instances you’re not using. You can also use this technique to guarantee that you have capacity available, even if you only use some of your reserved instances all the time.

b. Scaling pods

As your application or website grows, you’ll need to scale your pods to accommodate the increased traffic. Scaling pods up or down based on traffic conditions can be a great way to ensure that your site is always available and responsive, even during spikes in traffic. You’ll first need to create a Horizontal Pod Autoscaler (HPA) to set up autoscaling. An HPA will scale your pods based on a specified metric, such as CPU usage. Once you’ve created your HPA, you can specify the conditions under which it should scale up or down. For example, you might want to scale down when traffic is low and scale up when traffic is high. 

c. Use instance weighted scores

Different instance types have different computing, memory, and storage capabilities, which can impact the performance of your applications. To ensure that your application consistently performs at its best, you can use instance-weighted scores to autoscale your Kubernetes nodes. Instance-weighted scores consider the characteristics of the node instance type so that nodes with better performance are given a higher score.

d. Don’t mix VPA and HPA on either CPU or memory.

While VPA and HPA can be used together, mixing them on CPU or memory resources is not recommended. CPU resources should be autoscaled using VPA, while memory resources should be autoscaled using HPA.

Because VPA and HPA use different algorithms to determine when to scale up or down, mixing them can lead to suboptimal results. Mixing VPA and HPA on the same resource can lead to conflict and cause one or both autoscaling mechanisms to function incorrectly. If you’re using VPA and HPA together, it’s best to use them on separate resources. CPU for VPA and memory for HPA will ensure both autoscaling mechanisms operate optimally.

e. Node auto-provisioning

As your Kubernetes deployment scales, manually provisioning nodes becomes increasingly tedious and error-prone. With node auto-provisioning, you can define policies that automatically add new nodes to your deployment when needed. This can take the form of scaling up when CPU utilization is high or adding nodes in advance of a known spike in traffic. Not only does node auto-provisioning make it easy to scale your deployment, but it can also help improve availability and reduce costs. You can avoid paying for unused compute resources by automatically adding nodes only when needed. 

Conclusion

In the age of containerization and Kubernetes, resource management has become a critical issue for enterprises. StormForge is a tool that streamlines the management of Kubernetes resources, making it easy to deploy and manage containerized applications at scale. With StormForge, you can easily automate the management of your Kubernetes resources, making it simpler to deploy and manage containerized applications. StormForge also provides extensive monitoring and debugging capabilities, so you can troubleshoot issues quickly and resolve them efficiently. If you’re looking for a tool to help you manage your Kubernetes resources, StormForge is a great option. It’s easy to use and provides a wealth of features to make resource management simpler and more efficient.

If you have questions related to this topic, feel free to book a meeting with one of our solutions experts, mail to sales@amazic.com.

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