How do you monitor Kubernetes clusters for resource utilization?

Monitoring Kubernetes resource utilization involves continuously tracking the consumption of resources such as CPU, memory, and storage in the cluster. This is crucial for ensuring stable application performance, preventing resource exhaustion, and optimizing costs, especially in cloud-native environments that support auto-scaling and high availability scenarios.

The core components include deploying metrics collection tools (such as Prometheus and kube-state-metrics) and visualization components (such as Grafana). The principle is based on real-time collection of node and pod-level metrics, including request and limit values. In practical applications, this helps diagnose bottlenecks, implement Horizontal Pod Autoscaler (HPA), improve cluster efficiency, and reduce operational overhead.

Implementation steps: First, install the monitoring stack (for example, via Helm), and then configure resource metrics alert rules. Typical scenarios include application performance tuning and cost analysis. Business value is reflected in optimizing resource allocation, preventing downtime, and thereby driving operational efficiency.