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.
Continue reading Kubernetes CPU Limits: How to Manage Resource Allocation — FoxuTech
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