Understanding Kubernetes CPU CFS Throttled Periods

Table of Contents

1. Core Concepts
2. Typical Usage Example
3. Common Practices
4. Best Practices
5. Conclusion
6. References

Core Concepts

CFS Scheduler

The Completely Fair Scheduler is the default CPU scheduler in the Linux kernel. Its primary goal is to distribute CPU time fairly among all tasks. It uses a virtual runtime concept to keep track of how much CPU time each task has consumed. Tasks with lower virtual runtimes are given priority to run on the CPU.

CPU Limits in Kubernetes

In Kubernetes, you can set CPU limits for containers using the limits.cpu field in the container specification. For example:

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  containers:
  - name: my-container
    image: nginx
    resources:
      limits:
        cpu: "500m"  # 500 millicores

This means that the container my-container is allowed to use a maximum of 500 millicores of CPU time.

CFS Throttling

When a container tries to use more CPU time than its allocated limit, the CFS scheduler throttles the container. Throttling means that the container is temporarily paused or restricted from using additional CPU resources.

CFS Throttled Periods

The CFS throttled periods metric records the number of times a container has been throttled. It provides information about how often the container has hit its CPU limit. You can access this metric using Prometheus or other monitoring tools that integrate with Kubernetes.

Typical Usage Example

Let’s assume you have a Kubernetes cluster running a set of microservices. One of the microservices, payment-service, is experiencing performance issues. You suspect that it might be due to CPU resource constraints.

Step 1: Check CPU Limits

First, you need to check the CPU limits set for the payment-service containers. You can use the following command to get the pod specification:

kubectl get pod payment-service -o yaml

Look for the limits.cpu field in the container specification.

Step 2: Monitor CFS Throttled Periods

Next, you need to monitor the CFS throttled periods metric for the payment-service containers. If you are using Prometheus, you can use the following query:

container_cpu_cfs_throttled_periods_total{pod="payment-service"}

If the value of this metric is increasing rapidly, it indicates that the payment-service containers are being throttled frequently, which means they are hitting their CPU limits.

Step 3: Take Action

Based on the monitoring results, you can take appropriate actions. For example, you can increase the CPU limits for the payment-service containers:

apiVersion: v1
kind: Pod
metadata:
  name: payment-service
spec:
  containers:
  - name: payment-service-container
    image: payment-service-image
    resources:
      limits:
        cpu: "1000m"  # Increase to 1000 millicores

Common Practices

Monitoring

Regularly monitor the CFS throttled periods metric for all your containers. This will help you identify containers that are hitting their CPU limits and experiencing performance issues. You can use tools like Prometheus, Grafana, or Datadog for monitoring.

Resource Allocation

When setting CPU limits for your containers, make sure to allocate enough resources based on the expected workload. However, avoid over - allocating resources as it can lead to inefficient resource utilization.

Autoscaling

Implement horizontal pod autoscaling (HPA) based on CPU utilization. HPA can automatically adjust the number of pods based on the CPU load, ensuring that your application has enough resources to handle the traffic.

Best Practices

Set Realistic Limits

Before setting CPU limits, conduct performance testing on your applications to understand their CPU requirements. Set limits that are realistic and based on the actual workload.

Use Resource Requests and Limits Together

In Kubernetes, it is recommended to set both resource requests and limits for your containers. The request indicates the minimum amount of resources the container needs, while the limit sets the maximum. This helps Kubernetes schedule pods more efficiently.

Analyze Throttling Patterns

Look for patterns in the CFS throttled periods metric. For example, if a container is being throttled only during peak hours, you can consider adjusting the resource allocation or implementing autoscaling to handle the increased load.

Conclusion

Kubernetes CPU CFS throttled periods are a valuable metric for understanding how your containers are utilizing CPU resources. By monitoring this metric, you can identify performance issues related to CPU resource constraints, optimize resource allocation, and ensure the stability of your applications. By following common practices and best practices, you can effectively manage CPU resources in your Kubernetes cluster.

References

1. Kubernetes Documentation: https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/
2. Linux Kernel Documentation on CFS: https://www.kernel.org/doc/Documentation/scheduler/sched-design-CFS.txt
3. Prometheus Documentation: https://prometheus.io/docs/introduction/overview/

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