Kubernetes CPU Limits: How to Manage Resource Allocation

In Kubernetes, CPU limits define the maximum amount of CPU resources a pod is allowed to consume on a host machine. They play a crucial role in ensuring efficient resource utilization, preventing performance bottlenecks, and maintaining application stability within your cluster.

Understanding CPU Requests and Limits

Each node in a Kubernetes cluster is allocated memory (RAM) and compute power (CPU) that can be used to run containers. A Kubernetes cluster defines a logical grouping of one or more containers into pods. You can then deploy and manage pods on top of your nodes.

When you create a pod, you typically specify the storage and networking that containers share within that pod. The Kubernetes scheduler finds a node that has the required resources to run the pod.

You can provide more information for the scheduler using two parameters that specify RAM and CPU utilization:

• CPU Requests: Indicate the minimum guaranteed CPU resources a container requires to function properly. The Kubernetes scheduler uses this information to place pods on nodes with sufficient capacity.
• CPU Limits: Set a maximum ceiling on CPU usage for a container, preventing it from hogging resources and impacting other workloads. The kubelet enforces these limits by throttling containers that exceed them.

Benefits of Using CPU Limits:

If you do not specify a CPU limit, the container can use all the CPU resources available on the node. This can cause containers with high CPU utilization to slow down other containers on the same node and use all available CPU, and may even cause Kubernetes components such as the kubelet to become unresponsive. The node then enters a NotReady state, causing its pods to be rescheduled on another node.

By setting limits on all containers, you can avoid most of the following problems:

• Prevents Resource Contention: Ensures fair allocation of CPU resources across pods, stopping any single container from monopolizing CPU power and starving others.
• Enhances Predictability: CPU limits promote consistent application performance by guaranteeing a minimum level of CPU availability. This is vital for maintaining Service Level Agreements (SLAs).
• Safeguards Against OOM Issues: By limiting memory usage alongside CPU limits, you can prevent containers with memory leaks from crashing the entire node and jeopardizing cluster stability.
• Optimizes Resource Allocation: CPU limits help identify over-provisioned clusters where resources are underutilized. This can lead to cost savings by right-sizing your cluster resources.

Potential Drawbacks and Best Practices:

• Resource Underutilization: Setting overly restrictive CPU limits can prevent containers from leveraging available CPU cycles, leading to inefficient resource usage.
  • Best Practice: Carefully consider historical CPU usage patterns and benchmark your applications to determine appropriate CPU limit values.
• Complexity in Management: Implementing and managing CPU limits requires careful planning to avoid resource contention while ensuring containers have sufficient resources.
  • Best Practice: Utilize monitoring tools to track CPU usage across your cluster and fine-tune limits as needed.
• Starvation and Throttling: Excessively low CPU limits can lead to container starvation, where processes don’t receive adequate CPU time for execution. This can particularly impact compute-intensive workloads. Additionally, throttling containers that reach their limits can cause performance degradation.
  • Best Practice: Reserve appropriate CPU limits based on workload requirements. Consider using CPU classes or quota to provide differentiated CPU access tiers for various workloads within your cluster.

When to Use CPU Limits:

• Multi-tenant Environments: CPU limits are essential when running multiple applications or teams on a shared cluster. They guarantee fair resource allocation and prevent any single tenant from consuming an excessive share of CPU resources.
• Predictable Performance: For applications requiring guaranteed performance levels or adhering to strict SLAs, CPU limits ensure consistent resource availability and prevent unexpected performance fluctuations.
• Workload Bursts: If your workloads experience unpredictable spikes in CPU usage, setting CPU limits can prevent them from impacting the performance of other critical applications on the same node.

Identifying Pods Without CPU Limits and Monitoring Resource Usage:

Several techniques exist to identify pods lacking CPU limits and monitor resource utilization:

• Use queries like sum by (namespace,pod,container)(kube_pod_container_info{container!=""}) unless sum by (namespace,pod,container)(kube_pod_container_resource_limits{resource="cpu"}) to find containers without CPU limits within a specific namespace.
• Identify containers with tight CPU limits that might experience throttling by using queries like (sum by (namespace,pod,container)(rate(container_cpu_usage_seconds_total{container!=""}[5m])) / sum by(namespace,pod,container)(kube_pod_container_resource_limits{resource="cpu"})) > 0.8.
• Monitor cluster resource availability to avoid overcommitting resources. Ensure the sum of all resource requests across pods doesn’t exceed the total resources available on your cluster nodes.

Quick Tutorial: Assigning CPU Resources to Pods in Kubernetes

This guide walks you through assigning CPU resources to containers within a pod using Kubernetes.

1. Create a Separate Namespace (Optional):

While not strictly necessary, creating a dedicated namespace helps isolate the resources created in this tutorial from your existing ones. You can do this using the kubectl command:

kubectl create namespace cpu-example

2. Create a Pod with Resource Requests and Limits:

Here’s a pod YAML template with a single container. We’ll specify CPU requests and limits for this container:

apiVersion: v1
kind: Pod
metadata:
  name: cpu-demo
  namespace: cpu-example (if you created a namespace)
spec:
  containers:
  - name: cpu-demo-ctr
    image: sample-image  # Replace with your desired image
    resources:
      requests:
        cpu: "0.5"  # Request 0.5 CPU cores (minimum guaranteed)
      limits:
        cpu: "1"    # Limit CPU usage to 1 core (maximum allowed)

3. Apply the Pod Configuration:

Use kubectl apply to create the pod in your cluster (or the specified namespace):

kubectl apply -f my_sample_pod.yaml

Replace my_sample_pod.yaml with the actual filename where you saved the pod YAML configuration.

4. Verify Resource Requests and Limits:

You can check the pod’s resource requests and limits using kubectl get pod:

kubectl get pod cpu-demo -o yaml  # View detailed pod information

# Look for the "resources" section in the output

5. Monitor CPU Usage:

Use kubectl top pod to view real-time CPU usage of your pod:

kubectl top pod cpu-demo

This will display the name, CPU usage, and memory usage of your pod.

Explanation:

• resources.requests: This section defines the minimum CPU resources a container requires to function properly. The scheduler uses this to place pods on nodes with sufficient capacity.
• resources.limits: This section sets a maximum ceiling on CPU usage, preventing the container from exceeding its allocation and impacting other workloads.

Note:

• The args section in the example (--cpus 2) doesn’t directly affect CPU resource allocation. It’s a suggestion to the container about how many CPU cores it might ideally use. Resource requests and limits take precedence.
• Consider replacing vish/stress with the actual image you want to run in your container.

In Conclusion:

CPU limits are a valuable tool for managing resource allocation and maintaining application stability within your Kubernetes cluster. By carefully considering their benefits and potential drawbacks, and implementing best practices for setting and monitoring limits, you can optimize resource utilization, prevent performance bottlenecks, and ensure a healthy and efficient Kubernetes environment.