Identifying Kubernetes Pods Likely to Be Terminated First Under Resource Constraints

In a Kubernetes cluster, resource management is crucial for ensuring the stability and performance of your applications. When nodes run out of CPU or memory, Kubernetes prioritizes which Pods to terminate first based on their resource requests and limits. This guide will show you how to identify the Pods that are most likely to be terminated first if your cluster experiences resource constraints.

Step 1: Understanding Kubernetes QoS Classes

Kubernetes assigns a Quality of Service (QoS) class to each Pod based on its resource requests and limits. There are three QoS classes:

• Guaranteed: Pods that have both CPU and memory requests and limits set, and where the requests equal the limits.
• Burstable: Pods that have at least one request or limit set, but not both.
• BestEffort: Pods that have no requests or limits set.

Pods with the BestEffort class are the first to be terminated under resource pressure, followed by Burstable Pods. Guaranteed Pods are the last to be terminated.

Step 2: Checking Current Pod Resources

To identify the Pods in your project-c13 namespace that are most likely to be terminated first, we first need to inspect their resource requests and limits.

Use the following command to describe the Pods and check their resource requests:

This will show the resource requests (if any) for each Pod. Pods without any resource requests are considered BestEffort.

Step 3: Identifying BestEffort Pods

To automate the process of finding Pods without resource requests, you can use the following kubectl command:

This command lists all Pods along with their QoS class. Pods with the BestEffort class are those without any resource requests or limits, and these are the Pods most likely to be terminated first.

Step 4: Writing the Results to a File

Once you’ve identified the BestEffort Pods, write their names to a file as required by the task:

These are the Pods that would likely be terminated first if the nodes in your cluster run out of CPU or memory resources.

Conclusion

Identifying and managing resource requests and limits is crucial for maintaining a stable Kubernetes cluster. By setting appropriate resource requests and limits, you can ensure that your critical applications are less likely to be terminated during resource constraints, helping to maintain overall system stability and performance.