Running a Kubernetes cluster can be expensive, especially if you’re not using the right-sized resources. If you’re looking to save on costs, it’s important to right-size your Kubernetes resources. Over-provisioning resources is one of the biggest mistakes you can make when running a Kubernetes cluster. Not only will it cost you more money, but it can also lead to sub-optimal performance.

Fortunately, there are a few ways to save on Kubernetes costs without sacrificing performance. This post will discuss five ways to right size Kubernetes resources for cost savings.

What Are Resource Limits, and Why Do They Matter?

Resource limits are an important part of any Kubernetes cluster. By limiting the number of resources each pod can use, you can ensure that your cluster always has enough resources to meet the demands of your applications. There are two resource limits: CPU and memory, which prevents a pod from using more than a certain amount of CPU and memory resources.

There are two main reasons why resource limits are significant: 

First, resource limits help prevent one application from monopolizing the resources of your entire Kubernetes cluster. By capping the number of resources an application can use, you can ensure that other applications always have enough resources to run properly.

Second, resource limits can help you avoid out-of-memory errors. If a pod is using more memory than its limit, Kubernetes will kill the pod to prevent it from impacting the rest of the cluster.

6 Ways to Right-size Kubernetes resources

There are many factors to consider when determining the right size of Kubernetes resources. You can adjust the number of nodes, the amount of CPU and memory, and the number of pods.

Container settings

1. CPU

You can avoid over-provisioning or under-provisioning by right-sizing your containers, leading to wasted resources. Furthermore, you can fine-tune your Kubernetes resources by adjusting the number of replicas for your deployments. If you have too many replicas, your application may not be able to handle the load.

You’re probably over-provisioning if you’re not using all the CPU or memory allocated to your container. On the other hand, if your container is constantly maxing out its resources, you may need to increase its allocations.

2. Memory

As your Kubernetes applications grow, so do their memory requirements. If you’re not careful, you can quickly find yourself running out of memory and scaling your cluster just to keep things running. To avoid this, it’s important to right-size your Kubernetes resources by giving each application the amount of memory it needs to run, no more and no less.

Monitor your applications and collect memory usage data over time to analyze how much memory your applications use. Once you have that data, you can size your Kubernetes resources and avoid over-allocating memory.

3. Nodes

Another key setting is the maximum number of pods per node. This setting depends on the size of your nodes and the resources each pod requires. If you set this too high, you may overload your nodes and cause performance problems.

Namespace settings

4. ResourceQuotas

As organizations adopt Kubernetes, many find that the default settings for resource allocations are not well suited for their needs. This can lead to over-provisioning resources, wasting money, and impacting performance. ResourceQuotas allows you to specify the maximum amount of resources used by a given namespace. This ensures that one namespace cannot use more than its fair share of resources and helps to prevent “noisy neighbor” issues.

Resource quotas limit the number of resources that a particular user, group, or application can use. They can be used to limit the number of CPU cores, the amount of memory, or the number of storage volumes. One way to avoid this is to use ResourceQuotas to right-size your resources. By setting quotas, you can ensure that your pods receive the necessary resources without over-committing. 

5. LimitRange

To help optimize resource usage, Kubernetes provides the LimitRange object, which defines constraints on the maximum and minimum amount of resources a pod can request. By setting appropriate limits, you can help ensure that your applications are using the right amount of resources and not more.

In addition to setting hard limits on resources, LimitRange can also be used to define “recommended” or “default” values for resources. This can be helpful when you want to encourage users to request a certain amount of resources without making it mandatory.

6. Node Autoscaling

Node Auto-Scaling allows you to automatically scale the number of nodes in your Kubernetes cluster based on various metrics. This is a great way to ensure that your applications have the resources they need to function properly. To use Node Auto-Scaling, you must create a ClusterAutoscaler object. This object will contain the configuration for your Auto-Scaler. There are a few different parameters that you can configure, but the most important ones are the min and max node counts. These parameters specify the minimum and maximum number of nodes that your Auto-Scaler can scale to.

Conclusion

StormForge Automatic Kubernetes Resource Management is designed to help you manage your Kubernetes resources more efficiently. Kubernetes is a powerful container orchestration platform, but managing all the resources it uses can be challenging. That’s where Automatic Kubernetes Resource Management comes in. It’s a tool that helps you automatically manage your Kubernetes resources so that you can focus on more important tasks.

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.