Best Tips to Optimize Kubernetes Resources

 

 

 

 

Horizontal Pod Autoscaler (HPA) automatically adjusts the no of pods in  deployment or replica set based on observed CPU utilization or selected metrics so that your applications have the resources they required during peak times & save resources during off-peak times.

 

Example: 

 

apiVersion: autoscaling/v2beta2

kind: HorizontalPodAutoscaler

metadata:

  name: techinea-hpa

  namespace: techinea

spec:

  scaleTargetRef:

    apiVersion: apps/v1

    kind: Deployment

    name: techinea-ms

  minReplicas: 1

  maxReplicas: 20

  metrics:

  – type: Resource

    resource:

      name: cpu

      target:

        type: Utilization

        averageUtilization: 65

 

above mentioned configuration of HPA scales the techinea-ms(microservice) deployment up or down to maintain an average CPU utilization of 65%, to use resource efficiently.

 

 

Persistent storage can be a significant part of your Kubernetes costs,by optimizing your storage,such as using appropriate storage classes and dynamic provisioning to automatically create storage only when it’s required  to  reduce costs.

 

Example:

 

apiVersion: storage.k8s.io/v1

kind: StorageClass

metadata:

  name: techinea

provisioner: kubernetes.io/gce-pd

parameters:

  type: pd-standard

 

3.Using Pod Disruption Budgets

 

In Kubernetes,Pod Disruption Budgets (PDBs) help ensure that a minimum number of pods are available during voluntary disruptions like node maintenance(like kubernetes version upgrades or os patching,manual node restart,etc..),This ensures high availability without over-provisioning resources.

 

Example:

 

apiVersion: policy/v1

kind: PodDisruptionBudget

metadata:

  name: techinea-pdb

spec:

  minAvailable: 2

  selector:

    matchLabels:

      app: techinea-app

  

 

above mentioned configuration of PDB ensures that at least 2 instances of techinea-app are always available, prevents over-scaling while maintaining availability.

 

 

Kubernetes allows you to specify CPU and memory (RAM) requests &limits for each container,by properly configuring these parameters ensures that your applications have enough resources to run efficiently while preventing them from consuming excessive resources.

 

Example:

 

Version: v1

kind: Pod

metadata:

  name: brandmany

spec:

  containers:

  – name: brandmany-container

    image: brandmany/brandmany-microservice:22.9

    resources:

      requests:

        memory: “512Mi”

        cpu: “1024m”

      limits:

        memory: “1024Mi”

        cpu: “2024m”

 

above mentioned configuration specifies that the container needs at least 512Mi of memory and 1 CPU core to run & It also sets limits at 1024Mi of memory and 2 CPU core to prevent the container from using more than its proper share of resources,Regularly review resource metrics and adjust requests and limits accordingly

 

5. Implement Namespace Quotas and Limits

 

By setting quotas and limits at namespace level,we can control resource allocation & usage across different projects or teams,which prevents any single team from consuming resources.

 

Example: 

 

apiVersion: v1

kind: ResourceQuota

metadata:

  name: brandmany-project-quota

  namespace: brandmany

spec:

  hard:

    requests.cpu: “15”

    requests.memory: 25Gi

    limits.cpu: “30”

    limits.memory: 50Gi

 

above mentioned configuration limits the total amount of CPU and memory resources that can be requested or limited by all pods in the brandmany namespace,ensures proper resource distribution.

 

 

By managing network traffic Efficiently will reduce costs by ensuring resources are used optimally,implement network policies helps to prevent unnecessary cross talk between pods and can reduce the load on your network infrastructure.

 

apiVersion: networking.k8s.io/v1

kind: NetworkPolicy

metadata:

  name: access-techinea

spec:

  podSelector:

    matchLabels:

      app: techinea

  ingress:

  – from:

    – podSelector:

        matchLabels:

          access: “true”

 

 

 

Running multiple applications or services within the same Kubernetes cluster can maximize resource utilization and reduce overhead costs.Note: Multi-tenancy requires careful management of resources and security, but it can significantly increase efficiency.

 

 

Regular monitoring & analysis of your Kubernetes will cost more,Use monitoring tools (Prometheus,Grafana) and services that provide insights into where costs are incurred and identify 

 

 

9. Sizing Workloads (Size the Workloads Rightly)

it involves analyzing the resource utilization of your workloads and adjusting their allocations to better match their actual needs,over-provisioned workloads are waste of money, also under-provisioned workloads may lead to performance issues.

 

Check & Review regularly and delete any unused persistent volume claims (PVCs) to free up resources.