Metrics-server 0.6.3

• Resource utilization metrics: Metrics-server collects resource utilization data for CPU, memory, and network usage of Kubernetes nodes and containers.
• Lightweight and efficient: Metrics-server is lightweight and efficient, designed to run on large Kubernetes clusters without causing a significant impact on performance.
• Auto-scaling: Kubernetes can use metrics provided by Metrics-server to automatically scale resources based on demand.
• Cluster-wide aggregation: Metrics-server aggregates resource utilization data from all nodes and containers in the cluster, providing a comprehensive view of cluster resource usage.
• Customizable metrics queries: Users can query Metrics-server to obtain specific resource utilization metrics for nodes and containers.
• Security: Metrics-server supports authentication and authorization through Kubernetes RBAC, ensuring that only authorized users can access the collected metrics data.

• Auto-scaling: Metrics-server can be used to automatically scale resources in response to changing demand. For example, if a Kubernetes deployment experiences increased traffic, Metrics-server can provide usage metrics to Kubernetes, which can then automatically scale the deployment to meet the demand.
• Resource optimization: By monitoring resource utilization metrics provided by Metrics-server, Kubernetes administrators can optimize resource allocation across their clusters. For example, if a particular node is underutilized, workloads can be redistributed to other nodes to maximize resource utilization.

To use Metrics-server, the following steps can be taken:

1. Install Metrics-server: Metrics-server can be installed on a Kubernetes cluster using YAML manifests provided by the project.
2. Access metrics data: Once installed, Metrics-server will begin collecting resource utilization data from nodes and containers in the cluster. Metrics data can be accessed through the Kubernetes API.
3. Configure auto-scaling: Metrics-server can be used to configure auto-scaling for Kubernetes deployments by providing usage metrics to the Kubernetes Horizontal Pod Autoscaler (HPA) object.
4. Customize metrics queries: Users can query Metrics-server to obtain specific resource utilization metrics for nodes and containers using kubectl commands.
5. Ensure security: Metrics-server supports authentication and authorization through Kubernetes RBAC, so it is important to ensure that only authorized users have access to the collected metrics data.

• Metrics-server is written in Go and runs as a pod in a Kubernetes cluster.
• It collects resource utilization data from Kubernetes nodes and containers using the Kubernetes Metrics API.
• Metrics-server uses the Kubernetes aggregation layer to aggregate resource utilization data from all nodes and containers in the cluster.
• It provides resource utilization metrics for CPU, memory, and network usage.
• Metrics-server supports authentication and authorization through Kubernetes RBAC.