What approaches and practices do you have for resource management in Kubernetes when computational resources are limited?

When computational resources are limited in Kubernetes, it is important to manage resources efficiently to avoid overloads and downtime. Key approaches:

• Resource requests and limits: set minimum and maximum values for CPU and memory for containers so Kubernetes can properly schedule pods.
• Horizontal Pod Autoscaler: allows increasing or decreasing the number of replicas based on load.
• Vertical Pod Autoscaler: automatically adjusts the resources of a pod.
• Using QoS classes: ensures prioritized resource distribution.
• Container image optimization: reducing size and optimizing applications for lower resource consumption.
• Monitoring and alerting: use tools like Prometheus, Grafana, and others to track resource usage and respond to anomalies.

Example of resource configuration in a pod manifest:

apiVersion: v1
kind: Pod
metadata:
  name: example-pod
spec:
  containers:
  - name: example-container
    image: example-image
    resources:
      requests:
        memory: "128Mi"
        cpu: "250m"
      limits:
        memory: "256Mi"
        cpu: "500m"