Recommendations page in the GCP VM Instances view
The Recommendations page in the GCP VM Instances view displays a set of recommended actions that can help you resolve capacity-related problems in your Google cloud environment.
To access the page, in the navigation pane of the Views tab, click Views > Cloud > GCP > GCP VM Instances. The VM Instances page is displayed. Click the Recommendations tab.
Based on the type of problem in the infrastructure, a recommendation type is defined. For more information about all the recommendation types, and the configurable parameters and thresholds that are used to generate them, see Using recommendations to resolve capacity risk and efficiency issues.
To view a summary of recommendations for all VM instances
The Recommendations table displays detailed recommendation information for all the GCP VM instances. The following table describes each column in detail:
|Severity or Efficiency icon|
Every recommendation has an Efficiency icon associated with it. The icon indicates the level of efficiency improvement. It is based on the estimated savings (in $) per month that you can achieve by implementing the recommended actions. One of the following icons is displayed:
Icon - Efficiency level
The thresholds for calculating the efficiency level is defined in the associated optimizer rule.
Click the icon to view the recommendation details.
|Name||Name of the system for which the recommendation is generated. Click the name link to open the details page.|
|System Type||Type of the system. For example, Virtual Machine - GCP.|
|Type||Denotes the type of problem. For example, Idle VM.|
Brief description of the problem type. For example: This VM instance has been detected to be idle for more than 25 days.
Suggested actions to solve the problem. For example: Terminate this VM instance to save $32 per month.
To find a specific system
Use the sorting and filtering options on the page to find a specific system. For more information, see Sorting tables in views on the Helix Capacity Optimization Dashboard and Using filtering options in views on the Helix Capacity Optimization Dashboard.
Additionally, you can use the following filtering options on this page.
|Severity quick filter|
Enables you to quickly filter out systems to display details for systems based on their severity of risk and efficiency. A filter button for each of the following severity levels is available:
For example, if you apply Critical Risk filter, only systems at critical risk are displayed and you can review recommendation details for them.
Use this filter to select a specific system type.
To review recommendations details
To view the recommendation details for a VM, click the corresponding Efficiency icon (For example.
The Recommendations details window provides the exact findings along with the recommended actions that you can take to manage the VM.
The following table explains the different fields that you see in the Recommendations window.
|System||Name of the VM instance|
|Optimizer findings||Brief description of the issue|
|Type||Recommendation type. It is one of the configuration parameters in the optimizer rule that generates this recommendation|
|Severity/Efficiency||Efficiency level of the recommendation that is based on the estimated savings per month that you can achieve by implementing the recommended action|
|Generated on||Date of the last run of the associated optimizer rule|
|Optimizer rule||Name of the associated optimizer rule|
|Recommended actions||Suggested actions that you can implement to optimize costs|
(Displayed only for overallocated VM) Indicates the level of accuracy or reliability of the resizing recommendation. For more information about how the recommendation is generated, see Overallocated VM recommendation.
You can click the icon to view its description.
The icon can have one of the following values:
Value - Accuracy level
- Very High
For more information about each accuracy level, see Accuracy levels of resizing recommendations for overallocated VMs.
For more information about recommendations, see Using recommendations to resolve capacity risk and efficiency issues.