Kubernetes CPU Overcommit: A Comprehensive Guide
Table of Contents
Core Concepts
CPU Requests and Limits
In Kubernetes, each pod can specify both CPU requests and limits. A CPU request is the amount of CPU resources that the pod needs to run, and Kubernetes uses this information to schedule the pod on a node with sufficient resources. A CPU limit, on the other hand, is the maximum amount of CPU resources that the pod can consume. If a pod tries to consume more CPU than its limit, it may be throttled by the kernel.
Overcommit Principle
CPU overcommit works by allowing the sum of CPU requests across all pods in a cluster to exceed the total physical CPU resources available on the nodes. This is possible because not all pods will be using their full requested CPU resources at the same time. For example, some pods may be idle or have a low CPU utilization most of the time. By overcommitting CPU resources, Kubernetes can make more efficient use of the available hardware and run more pods on the same set of nodes.
Quality of Service (QoS) Classes
Kubernetes defines three QoS classes for pods based on their CPU requests and limits:
- Guaranteed: Pods with equal CPU requests and limits are classified as Guaranteed. These pods are guaranteed to have the specified amount of CPU resources and will not be throttled unless the node is under extreme resource pressure.
- Burstable: Pods with CPU requests less than their limits are classified as Burstable. These pods can use more CPU resources than their requests if available, but they may be throttled if the node runs out of resources.
- BestEffort: Pods with no CPU requests or limits are classified as BestEffort. These pods have the lowest priority and may be evicted first if the node runs out of resources.
Typical Usage Example
Let’s assume we have a Kubernetes cluster with three nodes, each having 4 CPU cores. We want to deploy a set of pods that have different CPU requirements.
Step 1: Create a Deployment
First, we create a deployment with a set of pods. Here is an example of a deployment YAML file:
apiVersion: apps/v1
kind: Deployment
metadata:
name: cpu-overcommit-example
spec:
replicas: 10
selector:
matchLabels:
app: cpu-overcommit-example
template:
metadata:
labels:
app: cpu-overcommit-example
spec:
containers:
- name: example-container
image: nginx:1.14.2
resources:
requests:
cpu: "0.2"
limits:
cpu: "0.5"
In this example, each pod requests 0.2 CPU cores and has a limit of 0.5 CPU cores.
Step 2: Analyze Resource Usage
The total CPU requests for all 10 pods in the deployment is 10 * 0.2 = 2 CPU cores. The total CPU limits are 10 * 0.5 = 5 CPU cores. Since the total physical CPU resources in the cluster are 3 * 4 = 12 CPU cores, we can overcommit the CPU resources and schedule these pods on the nodes.
Step 3: Monitor Performance
After deploying the pods, we can monitor their CPU usage using tools like Prometheus and Grafana. If the pods are not using their full requested CPU resources, we can further increase the number of replicas in the deployment to make more efficient use of the available resources.
Common Practices
Analyze Workload Characteristics
Before overcommitting CPU resources, it is important to analyze the workload characteristics of the pods. Some workloads, such as batch jobs or background tasks, may have a low and sporadic CPU utilization, making them good candidates for overcommit. On the other hand, real-time or CPU-intensive workloads may require more dedicated resources and may not be suitable for overcommit.
Set Appropriate QoS Classes
Based on the workload characteristics, set the appropriate QoS classes for the pods. Guaranteed pods are suitable for critical applications that require a consistent amount of CPU resources, while Burstable and BestEffort pods can be used for less critical applications or workloads with variable CPU requirements.
Monitor Resource Usage
Regularly monitor the CPU usage of the pods and nodes in the cluster. Use tools like Prometheus, Grafana, or the Kubernetes Dashboard to track resource utilization and identify any potential issues. If the CPU utilization of a node is consistently high, it may be a sign that the overcommit ratio is too high and needs to be adjusted.
Best Practices
Start with a Conservative Overcommit Ratio
When first implementing CPU overcommit, start with a conservative overcommit ratio, such as 1.5 or 2. This means that the total CPU requests of all pods in the cluster should not exceed 1.5 or 2 times the total physical CPU resources available on the nodes. As you gain more experience and confidence in your workloads, you can gradually increase the overcommit ratio.
Use Horizontal Pod Autoscaling (HPA)
Horizontal Pod Autoscaling (HPA) is a powerful feature in Kubernetes that allows you to automatically scale the number of pods in a deployment based on CPU utilization or other metrics. By using HPA, you can ensure that the pods have enough resources to handle the workload while also making efficient use of the available hardware.
Implement Resource Quotas
Resource quotas can be used to limit the total amount of CPU resources that can be requested by pods in a namespace. This helps to prevent overcommitting resources at the namespace level and ensures that each namespace has a fair share of the available resources.
Conclusion
Kubernetes CPU overcommit is a powerful technique that can help you make more efficient use of your hardware resources and reduce costs. However, it also requires careful planning and monitoring to ensure that the pods have the necessary resources to run smoothly. By understanding the core concepts, following common practices, and implementing best practices, you can effectively use CPU overcommit in your Kubernetes cluster and achieve better resource utilization.