Kubernetes CPU Limits: How They Work and Should You Avoid Them?

What Are Kubernetes CPU Limits? 

Kubernetes CPU limits specify the maximum amount of CPU resources that a container can use. These limits prevent a container from consuming excessive CPU, which otherwise might affect other containers within the same node. By defining CPU limits, Kubernetes ensures that each container operates within its defined resource boundaries, promoting fair CPU resource usage across all services.

When a container exceeds its CPU limit, Kubernetes throttles the CPU usage, meaning it temporarily limits the CPU resources available to the container. This mechanism ensures that the system remains stable and that no single container monopolizes CPU resources, thereby supporting overall system performance and responsiveness.

This is part of a series of articles about Kubernetes architecture.

In this article:

What Is the Kubernetes CPU Limit Used For?

CPU limits in Kubernetes are primarily used to manage resource allocation when multiple containers run on the same physical hardware. Setting limits ensures that each container has access to enough CPU resources to perform its tasks while preventing any container from affecting the performance of others. This is especially important in environments with high variability in workload demands.

Additionally, CPU limits help in budget control by preventing any single application from using more resources than allocated, which is crucial in cloud environments where resources directly translate into costs. By capping CPU usage, organizations can predict and control the compute expenses associated with their Kubernetes clusters.

What Is CPU Throttling? 

CPU throttling in Kubernetes occurs when a container attempts to use more CPU resources than its allocated limit. This process involves temporarily reducing the container’s CPU usage, effectively slowing down the application’s execution. Throttling is Kubernetes’ way of enforcing configured CPU limits and maintaining a balanced resource usage across all active containers.

While essential for resource management, CPU throttling can lead to degraded application performance if not properly managed. Applications might experience increased response times or reduced throughput, which can impact end-user experience and overall system efficiency.

Are Kubernetes CPU Limits an Antipattern?

Setting CPU limits on Kubernetes might sometimes be considered an antipattern, primarily because it can lead to undesirable CPU throttling. If limits are set too low, they can constrain application performance unnecessarily, affecting service responsiveness and potentially leading to timeouts and error escalations. 

The challenge is in accurately predicting the appropriate CPU limits that match an application’s demands without under or overprovisioning.

Applying CPU limits might lead to inefficient resource utilization. Resources that could be used by other workloads remain idle when hard limits are set, leading to a paradox where there are available resources but containers are throttled because they hit their predefined limits. This can result in a sub-optimal allocation of cluster resources.

Kubernetes CPU Limits vs. Kubernetes CPU Requests 

Kubernetes CPU limits dictate the maximum amount of CPU a container may use, whereas CPU requests specify the guaranteed minimum CPU resources the system should reserve for a container. While CPU limits aim at capping usage to prevent resource hogging, Kubernetes CPU requests focus on ensuring that a container has enough resources to start and run under normal conditions.

While requests ensure that a container has what it needs to run, limits make sure it does not exceed what is permissible, helping maintain a balanced load across the system. Both settings work together to optimize resource allocation and application performance within Kubernetes environments.

Best Practices to Avoid CPU Throttling in Kubernetes 

Here are some tips on how to avoid CPU throttling in Kubernetes.

Adjust or Remove CPU Limits

Adjusting or removing CPU limits may be necessary to prevent throttling and optimize application performance. If performance issues are detected, increasing or removing limits temporarily can determine if CPU constraints are causing the problem. However, it should be done cautiously to avoid resource starvation of other containers.

In scenarios where application workloads are unpredictable, removing CPU limits might make sense to provide flexibility. However, careful monitoring is essential to ensure that this does not lead to resource monopolization that could affect the overall health of the Kubernetes cluster.

Related content: Read our guide to kubernetes limits vs requests.

Prefer the Use of CPU Requests

Properly setting CPU requests is crucial to ensure that applications have the resources they need to perform optimally. Requests should be based on typical application needs rather than peak usage to avoid over-reservation of resources. This allows Kubernetes to more effectively schedule pods on nodes, ensuring sufficient CPU is available for each container’s baseline workload.

Using CPU requests helps prevent resource contention, improving overall cluster efficiency and reducing the likelihood of CPU throttling. It ensures that each container has enough CPU to operate effectively, contributing to smoother, more predictable application performance.

Control Threading in Applications

Controlling application threading can also prevent CPU throttling. Properly managing how threads are handled within an application ensures that it does not exceed CPU limits unexpectedly. Developers can optimize code to better distribute workload across available CPUs, or use threading libraries that are aware of the container’s CPU limits.

Applications can be designed to scale their threading behavior based on the available CPUs, which can be dynamically queried from within the Kubernetes environment. This adaptive approach allows applications to maximize their performance without risking CPU throttling.

Adjust CFS Tunables on Nodes

Adjusting the Completely Fair Scheduler (CFS) tunables on Kubernetes nodes can help fine-tune how CPU time is allocated among different processes, reducing the likelihood of throttling. Parameters like cfs_period_us and cfs_quota_us can be tweaked to allow containers more lenient CPU time slices, thus accommodating bursts in CPU demand more gracefully.

These adjustments need careful handling to avoid negative impacts on other nodes or container workloads. Ideally, changes should be tested in a controlled environment before rolling out to production to ascertain the benefits versus potential risks.

Use Horizontal Pod Autoscaling (HPA)

Implementing Horizontal Pod Autoscaling allows Kubernetes to automatically adjust the number of pod replicas based on observed CPU utilization. By scaling out an application during high-demand periods, CPU loads can be distributed across more instances, avoiding throttling in any single instance.

In addition to helping manage CPU spikes efficiently, HPA also improves application resilience and availability. It utilizes real-time metrics to make scaling decisions, ensuring that the deployment can adapt swiftly to changing demands, thus maintaining performance without manual intervention.

Learn more in our detailed guide to kubernetes hpa.

Automating Kubernetes Infrastructure with Spot by NetApp

Spot Ocean from Spot by NetApp frees DevOps teams from the tedious management of their cluster’s worker nodes while helping reduce cost by up to 90%. Spot Ocean’s automated optimization delivers the following benefits:

  • Container-driven autoscaling for the fastest matching of pods with appropriate nodes
  • Easy management of workloads with different resource requirements in a single cluster
  • Intelligent bin-packing for highly utilized nodes and greater cost-efficiency
  • Cost allocation by namespaces, resources, annotation and labels
  • Reliable usage of the optimal blend of spot, reserved and on-demand compute pricing models
  • Automated infrastructure headroom ensuring high availability
  • Right-sizing based on actual pod resource consumption  

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