DocumentationGrafana CloudMonitor infrastructureKubernetes MonitoringOptimize resource usage and efficiency

Optimize resource usage and efficiency

Kubernetes resources that aren’t optimized can significantly impact both budget and performance:

• An underprovisioned Kubernetes infrastructure leads to lagging, underperforming, unstable, or non-functioning applications.
• An overprovisioned infrastructure becomes costly.

To manage CPU, RAM, and storage, and mitigate the threat of an unstable infrastructure, you must:

• Ensure that there are enough allocated resources. This decreases the risk of Pod or container eviction as well as undesired performance of your microservices and applications.
• Eliminate unused or stranded resources.

Identify and prioritize efficiency issues

With Kubernetes Monitoring, you can effectively identify, prioritize, and handle efficiency-related issues. For example, start at the home page or the Alerts page to find containers that are causing alerts to fire due to exceeded thresholds for CPU requests and limits. Use the Usage switch on any list view to find, filter, and sort maximum values for CPU and memory usage.

Create an efficiency feedback loop

With Kubernetes Monitoring, you can discover:

• Resource-intensive workloads and Pods
• Unused and stranded resources in your fleet
• Stability issues created by incorrect configuration of requests and limits for CPU and memory usage
• How current data compares with historical data using the time range selector

Using this data, you can:

• Iteratively solve problems caused by incorrect configuration.
• Improve Pod and namespace placement to optimize the usage of the resources already available.
• Manage the availability of resources among your Clusters.
• Identify imperfections in resource management policy.

View efficiency data throughout the app

The sections of the home page show any usage and capacity thresholds that have been exceeded for Node CPU and disks, Node memory, and Persistent Volumes.

On any page with a list, click Usage to show CPU and memory usage for each item in the list. When you view the Namespaces, Workloads, and Nodes lists, the Cluster column is filtered by default for the first Cluster in alphabetical order, to decrease page load time, as shown in the following screenshot.

Discover usage over time

Use the time range selector to identify resource usage peaks and avoid performance degradation.

What is calculated

Kubernetes Monitoring uses the following to determine the calculation of unused resources, stranded resources, and resource limits:

• Unused resources: The average percentage of CPU, RAM, and storage that remain untouched for a certain period. When calculating unused resources, any momentary peaks in resource usage are averaged over the time window. These peaks can be above what’s expected at certain times. Calculations use resource requests set in your Kubernetes environment per best practices.

• Stranded resources: Resources that become unusable due to bad Pod placement. When too many CPU-intensive Pods exist in one Node, this strands RAM. Even if RAM is available and not used, the scheduler won’t assign these Pods due to insufficient CPU in the Node. For example:

  In this example, the team:

  • Paid for 10 GB RAM and 10 CPU (5 per Node per resource type)
  • Needed 8 GB RAM and 9 CPU
  • Used 5 GB RAM and 8 CPU
  • Still had their Pod evicted

• Resource limits: The Kubernetes configuration that provides minimum and maximum resource constraints for a Pod or container.