Managing CPU and Memory Resources in Kubernetes

Kubernetes is an open-source platform developed for the automation of application containers' Deployment, scaling, and operations across private, public, and hybrid cloud environments. Organizations can also use Kubernetes to manage microservice designs. Most cloud providers quite easily realize the benefits of containerization and Kubernetes deployments. This allows application developers, IT system administrators, and DevOps engineers to automatically deploy, scale, maintain, schedule, and operate a large number of application containers across node clusters.

What are Managing CPU and Memory Resources?

Kubernetes managing the CPU and memory resources is not an easy task. CPU and memory resources specify the resources containers have access to, such as CPU and RAM. Managing CPU indicates the minimal requirements for stable operation, whereas limitations specify the maximum allowable. A CPU limit, on the other hand, specifies the maximum number of CPUs that a container can consume before throttling occurs.

How Does Kubernetes Manage CPU and Memory Resources?

• The CPU limit establishes an absolute maximum for the amount of CPU time that the container may utilize.
• Typically, a weighting is defined by the CPU request. Workloads with higher CPU requests are allotted more CPU time than workloads with lower requests when many containers are vying for resources on a contested system.
• The memory resources request is most commonly utilized during pod scheduling.
• The memory resources limit specifies the memory limit for that group. If the container attempts to allocate more memory than this limit, the Linux kernel's out-of-memory subsystem activates and typically intervenes by terminating one of the container's processes.

Why Manage CPU and Memory Resources in Kubernetes?

• Manage CPU and memory resources to enhance resource allocation. K8s uses them to allocate resources like CPU and memory to containers in a cluster.
• For example, specifying a request of one CPU and a limit of two CPUs ensures that your container always has at least one CPU available and can use up to two if necessary.
• Manage CPU and resources to increase container performance and assist in avoiding related difficulties.
• A high limit may cause the container to consume an excessive amount of resources, resulting in cloud waste.
• Setting suitable CPU and memory resources improves overall cluster stability. If your container's limit is too high for its memory utilization.

When to Managing CPU and Memory Resources?

• Multi-tenant environments: In scenarios where Kubernetes serves numerous tenants (different teams or applications sharing the same cluster resources), CPU limitations prevent any single tenant from consuming disproportionate CPU resources.
• Benchmarking: Benchmarking is running the application under multiple operating circumstances to determine the real CPU use across different states of application load.
• Predictability: CPU limitations improve the predictability of program performance by assuring a consistent allocation of CPU resources. This stability is critical for applications.

Implementation of Managing CPU and Memory Resources in Kubernetes

Here is the step-by-step procedure for managing CPU and memory resources in Kubernetes:

Step 1: Create a Deployment with Resource Requests and Limits

First, you have to make sure to create a deployment YAML file. yaml.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: resource-demo
spec:
  replicas: 2
  selector:
    matchLabels:
      app: resource-demo
  template:
    metadata:
      labels:
        app: resource-demo
    spec:
      containers:
      - name: demo-container
        image: nginx
        resources:
          requests:
            cpu: "500m"
            memory: "256Mi"
          limits:
            cpu: "1"
            memory: "512Mi"

Step 2: Check Resource Requests and Limits

Next, you need to verify the deployment specifics.

kubectl describe deployment resource-demo

Output:

Step 3: Create an HPA Resource

Now you must have the same file to generate the YAML file hpa. yaml.

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: resource-demo-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: resource-demo
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

Apply the HPA:

kubectl apply -f hpa.yaml

Output:

Step 4: Check HPA Status

Then you have to check the HPA's current status.

kubectl get hpa

Output:

Step 5: Check Resource Quota Status

You can view the resource quota details.

kubectl get resourcequota

Output:

Step 6: Install Metrics Server and verify Node Resource Usage

Then you have to install the metrics server and also you need to verify Node Resource Usage.

kubectl top nodes

Output:

Step 7: Check Pod Resource Usage

Lastly, you can check which resource usage of pods in Kubernetes.

kubectl top pods

Output:

Conclusion

This article provides a comprehensive overview of the Managing CPU and Memory Resources in Kubernetes. You can able to efficiently manage the resources in your Kubernetes cluster, improving the efficiency of applications and resource use. Keeping a Kubernetes environment healthy and functioning properly requires regular monitoring and modifications.