Kubernetes Infrastructure Autoscaling is a powerful feature that allows you to automatically scale the underlying infrastructure of your Kubernetes cluster based on the needs of your workloads. This can help you ensure that your applications have the resources they need to run smoothly and reliably, without over- or under-provisioning.
In Kubernetes, you can use autoscaling at two levels: at the pod level and at the cluster level. Pod-level autoscaling, also known as Horizontal Pod Autoscaling (HPA), allows you to scale the number of replicas in a deployment based on observed CPU utilization or other metrics. This can be useful when you have workloads that experience bursts of activity, as the HPA controller can automatically scale up the number of replicas to handle the increased load.
Cluster-level autoscaling, also known as Cluster Autoscaler (CA), allows you to scale the number of nodes in your cluster based on the needs of your workloads. This can be useful when you have a large number of pods that need to run simultaneously, as the CA will automatically add new nodes to the cluster to accommodate the additional pods.
One of the key benefits of Kubernetes Infrastructure Autoscaling is that it allows you to optimize your cluster for cost and performance. By scaling up or down based on actual usage, you can avoid paying for resources that are not being used, and you can also ensure that your applications have the resources they need to run efficiently.
Another benefit of Infrastructure Autoscaling is that it can help improve the reliability of your applications. If your applications experience an unexpected surge in traffic, the HPA and CA controllers can automatically scale up the necessary resources to handle the increased load. This can help prevent downtime and keep your applications running smoothly.
There are a few things to consider when setting up Kubernetes Infrastructure Autoscaling. First, you need to ensure that you have the necessary resources and permissions to create and delete nodes in your cluster. You also need to decide on the appropriate metrics to use for scaling, as well as the appropriate target utilization levels.
It’s also important to consider the impact of autoscaling on your applications. When the HPA or CA scales up the number of replicas or nodes, there may be a temporary impact on performance as the new resources are brought online. You may also need to consider the impact of scaling down, as removing replicas or nodes can affect the availability of your applications.
To set up Kubernetes Infrastructure Autoscaling, you will need to use the Kubernetes API or command-line tools such as
kubectl. You can use the
kubectl autoscale command to set up HPA, and you can use the
kubectl cluster-info command to view the current status of your cluster and the resources it is using.
Cluster Autoscaler is an open-source project that automatically scales a Kubernetes cluster based on the scheduling status of pods and resource utilization of nodes. If you have several pods that are unscheduled because of insufficient resources, Cluster Autoscaler will automatically add more nodes to the cluster using your cloud provider’s auto scaling capabilities–for example, Auto Scaling Groups (ASGs) and Spot Fleet within AWS or similar services in the case of Microsoft Azure or Google Cloud
Despite this simple approach to auto scaling, configuring Cluster Autoscaler for optimal use is complex. As a DIY solution, users need to have a good understanding of their pods and container needs, and need to be aware of the limitations (and related consequences) of Cluster Autoscaler:
- Overprovisioning is common as Cluster Autoscaler looks at defined resource requests and limits, not at actual resource usage
- Limited flexibility because although mixed instance types can be used in a node group, instances need to have the same capacity (CPU and memory)
- For customers that want to leverage different kinds of compute, managing multiple node pools is complex
- With no fallback to on-demand instances, cannot be used with spot instances without creating performance and availability risks
- Auto Scaling Groups need to be managed independently by the user
Default Solution vs Hands-off approach
These limitations of Cluster Autoscaler mean that, while it’s a good initial solution, it’s not always the best fit, especially when users are looking for strategies to take a more hands-off approach to infrastructure and reduce the cost of their cloud operations.
In summary, Kubernetes Infrastructure Autoscaling is a powerful tool that can help you optimize the cost and performance of your applications, while also improving their reliability. By automatically scaling the underlying infrastructure of your cluster based on actual usage, you can ensure that your applications have the resources they need to run smoothly and efficiently.