This documentation supports releases of BMC Helix Continuous Optimization up to December 31, 2021. To view the latest version, select the version from the Product version menu.

Kubernetes - Overallocated containers recommendation

The Overallocated containers recommendation identifies containers with underutilized resources (CPU or memory). 

You can configure a recommendation as per your requirement by modifying the condition in the corresponding Optimizer rule. A user with administrative privileges can reconfigure or add new Optimizer rules to modify the recommendations. For more information, see Configuring and managing Optimizer rules.

The following recommendation details are provided:

  • Brief description of the issue
  • Severity or Efficiency level of the recommendation. The thresholds for calculating the efficiency level is defined in the associated optimizer rule.
  • Date when this recommendation is generated on. It is the date of the last run of the associated optimizer rule.
  • Name of the associated optimizer rule
  • Recommended action that suggests reconfiguring the container with the ideal configuration values to reclaim unused resources. 
  • Actions that you can perform on your recommendations. For details, see Managing the recommendations.
  • Comparison between the current resource usage and the estimated future usage after reconfiguring the container
  • Criteria used for detecting the container as overallocated
  • Benefits of implementing the recommendation

A container is detected to be overallocated if the resource demand, demand peak, and the corresponding headroom values are lower than the configured request and limit values:

  • CPU Request Headroom
  • CPU Limit Headroom
  • Memory Request Headroom
  • Memory Limit Headroom

Demand is used for recommendation of request and demand peak is used for recommendation of limit. 

You can change the default values of these thresholds on the Rules page in the Administration > Optimizer section. 

For more information about modifying the Optimizer rules, see Configuring and managing Optimizer rules.

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