Kubernetes Resource Management: A Practical Guide

What Is Kubernetes Resource Management 

Kubernetes resource management involves overseeing the allocation and utilization of hardware resources within a Kubernetes cluster to ensure that applications run efficiently. This management is critical because Kubernetes operates based on a container orchestration system, which may need to handle thousands of containers running across different servers.

Managing Kubernetes resources requires the ability to track and control the use of CPU, memory, storage, and network resources. Kubernetes enables this through a series of abstractions and mechanisms such as pods, nodes, and the control plane.

By carefully managing these resources, Kubernetes administrators can ensure that applications have the resources they need when they need them, maintaining performance and cost efficiency.

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

In this article:

The Importance of Kubernetes Resource Management 

Resource management in Kubernetes ensures efficient utilization of hardware resources, leading to reduced operational costs. Proper management allows for the allocation of resources based on the specific needs of applications while maintaining the required level of performance and availability.

With effective resource management, Kubernetes can optimize the allocation and recycling of resources. This leads to enhancements in overall system performance, prevents resource starvation, and supports the scalability requirements of containerized applications across multiple environments.

Kubernetes Compute Resources

Here are some of the main compute resources to monitor in Kubernetes.


In Kubernetes, CPU resources are measured in CPU units. One CPU, in Kubernetes terminology, may refer to a hyperthread on a multicore machine or a single core, depending on the underlying architecture and cloud provider. Precise management of CPU resources ensures that applications have enough power to run without wasting resources that could be utilized by other applications.

Management tools allow administrators to set limits and requests for CPU usage. This means an application or container can specify the minimum resources it requires (requests) and the maximum it’s allowed to consume (limits). This helps prevent a single application from monopolizing CPU resources, crucial in multi-tenant environments.


Memory management in Kubernetes involves assigning and limiting the RAM available to pods and containers. Right-sizing prevents instances from consuming excessive memory which could affect other applications running on the node. Setting proper requests and limits is vital to avoid system crashes due to out-of-memory (OOM) issues.

Memory requests in Kubernetes enforce minimum guaranteed amounts, ensuring that applications have the memory they need to function correctly without interruption. Memory limits ensure that applications do not exceed a certain allocation, helping to maintain the overall health of the hosting server.

Ephemeral Storage

Ephemeral storage in Kubernetes refers to storage that is tied directly to the pod’s lifecycle. It is mainly used for temporary data that is closely associated with individual applications running on the platform, such as caching layers and scratch data. Managing ephemeral storage is crucial to prevent data loss when a pod is terminated or fails.

Kubernetes allows administrators to specify requests and limits for ephemeral storage in a similar way to CPU and memory. This ensures that the ephemeral storage used does not exceed its allocation, potentially impacting other processes. 

Other Types of Kubernetes Resources 

Kubernetes resources can also include non-compute resources.

Network Bandwidth

Network bandwidth in Kubernetes oversees the data transfer rate at which data can be sent over the network connections among nodes and their connections to outside networks. Managing bandwidth is especially important in distributed systems where communication-intensive operations are common, minimizing latency and congestion.

Ensuring an adequate allocation of bandwidth and prioritizing traffic based on application needs helps maintain consistent communication channels. Strategies such as Quality of Service (QoS) policies can be enforced to differentiate between types of traffic and prioritize crucial network transfers over lower priority data.


Disk input/output operations per second (IOPS) measures the rate at which data can be written and read from a storage device in the computing environment. Kubernetes supports controlling this aspect to optimize application performance and prevent potential bottlenecks caused by slow disk operations.

Setting proper IOPS limits per application can help maintain a balanced environment where no single app overwhelms the disk usage. This helps protect other applications’ performance and response times, which is critical in ensuring the stability and efficiency of services that rely heavily on persistent data operations.

GPU Acceleration

GPU acceleration in Kubernetes is useful for tasks that require parallelized compute operations such as machine learning, 3D rendering, or complex simulations. GPUs are managed as distinct resources, and their efficient management can enhance the speed of intensive computational applications.

By effectively allocating GPUs, Kubernetes allows applications that can benefit from GPU acceleration to perform better while ensuring GPUs are not wasted. Proper management includes assigning GPUs to the pods that need them and ensuring that these resources are optimally shared among tasks to maximize utilization.

Kubernetes Resource Management Challenges 

Here are some of the main challenges involved in managing Kubernetes resources.

Dynamic Workloads

Dynamic workloads can be challenging to manage due to their fluctuating resource demands. Such workloads can scale rapidly in response to increased demand, necessitating quick allocation and reallocation of resources. Kubernetes must monitor and swiftly respond to changes in resource demand to facilitate this.

Resource Fragmentation

Resource fragmentation occurs when system resources are inefficiently allocated, leaving fragments of untapped resources. In a Kubernetes environment, this can lead to reduced node performance and increased operational costs due to underutilized resources.

Noisy Neighbors

Noisy neighbors in Kubernetes refer to competing applications or containers that consume higher-than-anticipated resources, affecting the performance of other processes on the same node. This issue is predominantly seen in environments with shared resources, where it’s necessary to isolate processes to ensure fair resource distribution.

QoS Classes

Quality of Service (QoS) classes in Kubernetes help manage how resources are allocated to pods when there is resource contention. There are three classes: Guaranteed, Burstable, and Best-Effort, each offering different levels of resource certainty. Understanding and leveraging these classes can highly optimize how resources are utilized e.g., prioritizing critical applications to remain stable under any conditions.

Best Practices for Optimizing Kubernetes Resource Management 

Here are some measures that administrators can take to best manage resources in Kubernetes.

Set Resource Quotas and Limits

Resource quotas and limits control resource consumption across the cluster. They prevent overconsumption of resources by a single namespace, ensuring availability for others. Quotas set bounds on the cumulative resource request and usage per namespace, while limits specify the maximum amount an individual pod or container may use.

Applying these controls helps prevent resource contention and ensures fair resource distribution, crucial in multi-tenant environments. Proper use of quotas and limits preserves system stability and helps enforce policy and security compliance.

Use Taints and Tolerations

Taints and tolerations are features in Kubernetes that help ensure that pods are scheduled on appropriate nodes. A taint on a node means that no pod can be allocated there unless it has a matching toleration. This is useful for reserving resources for specific workloads or segregating workloads for security reasons.

Using taints and tolerations, cluster administrators can control where various types of workloads can run, enhancing performance and security. This also aids in maintaining cluster health by preventing the overloading of nodes and ensuring workload-specific resource availability.

Use Node and Pod Affinity and Anti-Affinity

Affinity and anti-affinity settings control how pods are distributed across the cluster’s nodes. Node affinity rules can attract pods to specific nodes, while anti-affinity rules can be used to spread out or isolate workloads from each other. This is particularly important for high-availability and fault-tolerant applications.

These settings help optimize resource usage and application performance by strategically locating workloads based on network topology and the specific requirements of different applications. Proper configuration of these rules can prevent performance bottlenecks.

Leverage Pod Priority and Preemption

Pod priority and preemption enable Kubernetes to schedule pods more effectively based on defined priorities. When resources are scarce, the system can preempt (remove) lower-priority pods to make room for higher-priority ones waiting to be scheduled. This ensures that critical applications receive the necessary resources to function properly.

Administrators can use priority classes to define the importance of each pod, which then influences the scheduling decisions to maintain service levels for prioritized workloads. This helps manage resource contention and maintain application performance and availability during peak demand periods.

Right-Size Pods

Right-sizing pods is about configuring them with the appropriate amounts of CPU and memory according to their actual usage, ensuring efficiency and preventing resource wastage. This involves analyzing performance metrics and adjusting requests and limits as necessary. 

Right-sizing helps achieve cost optimization while maintaining the desired level of performance.

Ongoing monitoring and right-sizing of pods allow for fine-tuned resource allocation that matches the exact needs of applications, leading to cost savings.

Use Horizontal and Vertical Autoscaling (HPA and VPA)

Horizontal and vertical autoscaling are mechanisms in Kubernetes that automatically adjust the quantity of pod replicas and the size of pods in reaction to workload changes. Horizontal autoscaling changes the number of pods, while vertical autoscaling adjusts the CPU and memory limits. These features help Kubernetes handle varying workloads efficiently.

Autoscaling ensures that applications have sufficient resources to handle increases in load and scales them down during low usage to save costs. Both types of autoscaling contribute to maintaining application performance, reducing manual intervention, and optimizing resource utilization.

Leverage Resource Monitoring and Optimization Tools

Monitoring and resource optimization tools offer insights into the usage and performance of Kubernetes resources, enabling informed decision-making regarding allocation and optimization. Common tools include Prometheus for monitoring and Grafana for visualization, providing a detailed view of the cluster’s state.

These tools help detect anomalies and inefficiencies, aiding in capacity planning and troubleshooting. Regular monitoring and optimization enable continuous improvement in resource management, helping administrators keep their Kubernetes environment cost-effective and performant.

Learn more in our detailed guide to Kubernetes monitoring 

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  

Learn more about Spot Ocean today!