The Kubernetes Cluster Autoscaler (CAS) is a tool for managing the resources of a Kubernetes cluster efficiently. It automatically adjusts the number of nodes in a cluster based on the demand of running workloads, ensuring sufficient resources for pods while optimizing costs by removing underutilized nodes. This article explores the key concepts, features, and setup processes of the Kubernetes Cluster Autoscaler to help you maintain a scalable and cost-effective Kubernetes environment.
Understanding the Cluster Autoscaler
What is the Cluster Autoscaler?
The Cluster Autoscaler (CAS) is designed to manage the size of a Kubernetes cluster based on the resource requirements of workloads. Its primary goal is to ensure that all pods have sufficient resources while minimizing waste by removing underutilized nodes. By monitoring resource usage and pod scheduling status, the CAS makes decisions to scale the cluster up or down based on the changing demands of applications.
How does the Cluster Autoscaler work?
The Cluster Autoscaler continuously monitors the Kubernetes API server for unschedulable pods. When it detects pods that cannot be scheduled due to insufficient resources, such as CPU or memory, it triggers a scale-up event. The CAS determines the best node group to expand based on factors like resource availability and cost efficiency, then provisions new nodes to accommodate the pending pods.
Conversely, when the CAS identifies underutilized nodes, it initiates a scale-down process. It verifies that all pods on the underutilized node can be safely moved to other nodes in the cluster. If the pods can be relocated and the node has been underutilized for a specified period (default: 10 minutes), the CAS cordons the node, drains the pods, and terminates the node. This process optimizes resource usage and reduces costs by eliminating unnecessary nodes.
The Cluster Autoscaler makes scaling decisions based on configurable parameters, such as resource utilization thresholds and the minimum and maximum number of nodes allowed in each node group. It also considers pod disruption budgets (PDBs) and pod priority to ensure that critical workloads are not disrupted during scaling.
Key Features of the Cluster Autoscaler
Resource-Conscious Scaling
The Kubernetes Cluster Autoscaler makes scaling decisions based on the resource requirements of pods. By analyzing resource usage and scheduling status, the CAS ensures there is enough capacity to run all pods while avoiding overprovisioning. This approach helps maintain a balance between application performance and cost efficiency.
Expanders for Optimized Node Group Selection
When a scale-up is necessary, the Cluster Autoscaler uses expanders to select the most appropriate node group to expand. Expanders are plugins that implement various strategies for choosing the best node group based on factors like resource availability and user-defined priorities. The CAS provides built-in expanders, including the default "random" expander and more advanced options like "most-pods" and "least-waste," which optimize for maximum pod scheduling and minimum resource waste.
Starting from version 1.23.0, the Cluster Autoscaler supports using multiple expanders in a hierarchical manner, allowing users to combine different strategies for more intelligent scaling decisions. For instance, you can prioritize certain node groups with the "priority" expander and then select the most resource-efficient option within those groups using the "least-waste" expander.
Respect for Kubernetes Constraints and Budgets
The Cluster Autoscaler is designed to work with Kubernetes' built-in constraints and budgets. It adheres to pod disruption budgets (PDBs), which specify the maximum number of pods that can be unavailable during a voluntary disruption, such as a node scale-down. By following PDBs, the CAS ensures that critical workloads remain available and unaffected during scaling.
Additionally, the Cluster Autoscaler considers pod priority and preemption when making scaling decisions. It prioritizes scaling node groups that can accommodate high-priority pods while avoiding disruption to lower-priority pods whenever possible.
Setting Up and Configuring the Cluster Autoscaler
To leverage the features of the Cluster Autoscaler, you need to set it up properly in your Kubernetes environment. There are two main approaches: autodiscovery and manual configuration.
Autodiscovery Setup
The autodiscovery setup is a simpler and more dynamic approach, particularly useful in environments where autoscaling groups change frequently. You tag your autoscaling groups (ASGs) with specific key-value pairs that the Cluster Autoscaler recognizes, allowing it to automatically identify and manage the relevant node groups.
To enable autodiscovery, create an IAM role with permissions to list, describe, and manage nodes in the ASGs. Then, tag your ASGs with the following key-value pairs:
k8s.io/cluster-autoscaler/enabled: “”
k8s.io/cluster-autoscaler/<CLUSTER_NAME>: “”
Replace <CLUSTER_NAME> with the name of your Kubernetes cluster. Finally, install the Cluster Autoscaler using a provided YAML manifest file, specifying the image version that matches your Kubernetes version and updating the --node-group-auto-discovery flag with your cluster name.
Manual Setup
The manual setup involves explicitly specifying the names of the autoscaling groups you want the Cluster Autoscaler to manage. This approach is better suited for static environments where the autoscaling groups are unlikely to change. While it requires more initial configuration, it provides precise control over which node groups the CAS manages.
To set up the Cluster Autoscaler manually, create an IAM role with similar permissions as in the autodiscovery setup. Instead of tagging the ASGs, specify the names of the desired autoscaling groups directly in the Cluster Autoscaler's deployment configuration.
Regardless of the setup approach, ensure that the Cluster Autoscaler has the necessary permissions to interact with the Kubernetes API server and the cloud provider's autoscaling APIs. Additionally, configure the CAS parameters, such as scaling thresholds and the minimum and maximum number of nodes per node group, to match your requirements and optimize scaling behavior for your workloads.
Conclusion
The Kubernetes Cluster Autoscaler is a valuable tool that simplifies node scaling management in a Kubernetes cluster. By automatically adjusting the number of nodes based on resource demands, the Cluster Autoscaler ensures that applications have the necessary resources while minimizing costs through the removal of underutilized nodes.
With features like resource-conscious scaling, support for multiple expander strategies, and adherence to Kubernetes constraints, the Cluster Autoscaler provides a robust solution for managing cluster resources. Whether you choose the autodiscovery or manual setup approach, the Cluster Autoscaler can be easily integrated into your Kubernetes environment, allowing you to focus on application development and deployment.
However, it's important to recognize that the Cluster Autoscaler may not always be the best choice for every use case. In some scenarios, alternative autoscaling solutions like Karpenter, which offers faster scaling and improved resource utilization, may be more suitable.
Top comments (0)