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VMware Tanzu CloudHealth
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VMware Tanzu CloudHealth Product Documentation
 * Working with Reports and Recommendations
   Working with Reports and Recommendations
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Docs / VMware Tanzu CloudHealth / Working with Reports and Recommendations


NEW AZURE RIGHTSIZING


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Updated on   02/12/2024
Selected product version: VMware Tanzu CloudHealth SaaS



NEW AZURE RIGHTSIZING


AZURE VM RIGHTSIZING

The new Rightsizing tool can help you use your Virtual Machines (VMs) more
efficiently and save money.

Using Rightsizing, you can:

 * Analyze the workload patterns of your VMs
 * Apply custom efficiency targets to generate recommendations for optimal VM
   sizes and to meet different application and enterprise requirements
 * Gain clear insight into your total cost saving opportunities
 * Fine-tune VM sizing based on up to two additional recommendations

Unlike the legacy Rightsizing tool, you can change efficiency targets and
instantly view updated recommendations to ensure the best fit.

To access our new Azure VM Rightsizing tool, navigate to Recommendations >
Rightsizing.


USING THE RIGHTSIZING DASHBOARD

The Rightsizing dashboard provides information about the efficiency of your VMs,
displays the status of each VM and provides recommendations for each VM.

The Efficiency Summary provides the following information:

 * Current Recommendation: the total number of recommendations available in the
   dashboard.
 * Current Monthly Price: the sum of the monthly cost for all VMs. VMware Tanzu
   CloudHealth considers actual usage hours to date for the whole month to
   calculate the monthly price for each current VM using on-demand hourly rates
   for the current VM.
 * Projected Monthly Price: the monthly cost when the best fit is applied to all
   VMs. If there are no recommendations, then the current VM’s hourly cost is
   used. Tanzu CloudHealth considers the same number of usage hours as the
   current VM and calculates the monthly price for all recommended VMs using
   on-demand hourly rates for the recommended VM.
 * Projected Monthly Savings: the difference between Projected Monthly Cost and
   Current Monthly Cost. This is calculated by subtracting the Projected Monthly
   Savings from the Current Monthly Cost.

You can set an efficiency target and date range to help generate targeted
recommendations. For more information about creating and using efficiency
targets, see Efficiency Targets.

You can also choose to include or exclude burstable VM in your recommendations.
Burstable VMs guarantee a baseline level of CPU performance but can also exceed
the baseline level of performance to sustain a high level of CPU performance as
needed.

 * Click All Filters to view more filter options for your recommendations, such
   availability zone, subscriptions, enrollments, resource groups and
   perspective.
 * Click Export to download a CSV file of the recommendations.


UNDERSTAND RECOMMENDATIONS

The Recommendations table shows all VMs that fit the selected filters and that
have a recommendation. You can also enable Show all recommendations to view VMs
that do not currently have a recommendation.

The dashboard displays the number of VMs with recommendations and their fit:

 * Good fit: the VM meets all efficiency targets.
 * Under target: the VM is underutilized and does not meet all efficiency
   targets. Tanzu CloudHealth provides a recommendation to optimize the VM.
 * Over target: the VM is overutilized and exceeds one or more efficiency
   targets.
 * Undetermined fit: one or more of the metrics is above target, but one or more
   of another metric is below target, so the fit could not be determined.
 * Not enough data: there was no data available for a metric for the selected
   time period, so the fit could not be determined.

The Recommendations table also displays the following information:

 * Current Price: The current cost of the VM.
 * Recommended Price: The estimated cost of the VM after applying the most
   cost-effective recommendation.
 * Estimated Savings: The estimated monthly savings after applying the most
   cost-effective recommendation.

> The prices for all VMs are calculated using the list or on-demand prices with
> no discounts applied, considering the actual usage hours of VMs.


TYPES OF RECOMMENDATIONS

There are two types of recommendations:

 * Resize: Identifies the right size candidate VM that can handle the usage for
   the current VM. Resizing recommendations can be made across different VM
   families.

 * Terminate: Identifies VMs that have less than one percent memory and CPU
   utilization, and can likely be terminated.

If Tanzu CloudHealth is unable to make a recommendation for a VM, it is hidden
from the dashboard. You can enable Show all recommendations to view VMs that do
not currently have a recommendation.

> No recommendations are generated in the following situations: - Accelerated
> computing VMs - If the VM has no options matching the efficiency target
> requirements - If metric data is unavailable for all metrics in the selected
> efficiency target


VIEW RECOMMENDATIONS

Click a VM name to view recommendation options and metrics for that VM. The
details page shows monthly cost for the VM, the primary recommendation and
provides up to two additional recommendation options.

The Metrics charts show current performance metrics and the target efficiency
range. You can use these charts to determine how your current usage compares to
the target metrics.

VIEW RECOMMENDATIONS BY SUBSCRIPTION OR PERSPECTIVE

The Rightsizing tool also gives you an option to view recommendations grouped by
subscription or perspective.

In the Group By dropdown, select Subscription and click Update. You will see all
the recommendations grouped by subscription. Expand a subscription name to view
all the VMs that fall into that specific account. Click the VM name to know the
recommendation details of the selected VM.



For grouping by Perspective, you need to select a perspective group for which
you want to view the recommendations. Once you click Update, you will see all
the recommendations grouped by perspectives. Expand a perspective name, and all
the VMs associated with that specific perspective appear. Further, you can
explore recommendations details by clicking the VM name.




KUBERNETES RIGHTSIZING

The new Rightsizing tool can help you use your Kubernetes Containers more
efficiently and save money. Using Rightsizing, you can:

 * Analyze the workload patterns of your containers
 * Apply custom efficiency targets to generate recommendations for optimal
   container sizes and to meet different application and enterprise requirements
 * Gain clear insight into your total cost saving opportunities
 * Fine-tune container sizing based on up to two additional recommendations

Unlike the legacy Rightsizing tool, you can change efficiency targets and
instantly view updated recommendations to ensure the best fit.

To access our new Kubernetes Rightsizing tool, navigate to Recommendations >
Rightsizing.


USING THE RIGHTSIZING DASHBOARD

The Rightsizing dashboard provides information about the efficiency of your
containers, displays the status of each container and provides recommendations.

The Efficiency Summary provides the following information:

 * Current CPU: the total requested compute capacity across all your containers
   (in CPU cores)
 * Recommended CPU: the total recommended compute capacity across all your
   containers (in CPU cores)
 * Current Memory: the total requested memory capacity across all your
   containers (in GB)
 * Recommended Memory: the total recommended memory capacity across all your
   containers (in GB)
 * No Change: total number of containers that do not need any change and thus,
   have no recommendations.
 * Resize: total number of containers that need to resize their CPU and/or
   Memory requests and thus, have recommendations.

You can set an efficiency target and date range to help generate targeted
recommendations. For more information about creating and using efficiency
targets, see Efficiency Targets.

Click All Filters to view more filter options for your recommendations, such as
clusters, namespaces and workload types.

Click Export to download a CSV file of the recommendations.


UNDERSTAND RECOMMENDATIONS

The Recommendations table shows all containers that fit the selected filters and
that have a recommendation. You can also enable Show all recommendations to view
containers that do not currently have a recommendation.

The dashboard displays the number of containers with recommendations and their
fit:

 * Good fit: the container meets all efficiency targets.
 * Under target: the container is underutilized and does not meet all efficiency
   targets. Tanzu CloudHealth provides a recommendation to optimize the
   container.
 * Over target: the container is overutilized and exceeds one or more efficiency
   targets.
 * Undetermined fit: one or more of the metrics is above target, but one or more
   of another metric is below target, so the fit could not be determined.
 * Not enough data: there was no data available for a metric for the selected
   time period, so the fit could not be determined.

The Recommendations table also displays the following information:

 * CPU Requests: the requested compute capacity for a container.
 * Memory Requests: the requested memory capacity for a container.
 * Recommended CPU Requests: the recommended compute capacity for a container
   based on applied efficiency target.
 * Recommended Memory Requests: the recommended memory capacity for a container
   based on applied efficiency target.


RESIZE RECOMMENDATIONS

Identifies the right amount of compute and/or memory requests that can handle
the usage for the current container.

If Tanzu CloudHealth is unable to make a recommendation for a container, it is
hidden from the dashboard. You can enable Show all recommendations to view
containers that do not currently have a recommendation.

Note: No recommendations are generated in the following situations: - If the
instance is a host tenant - Accelerated computing or bare metal instances VMs -
If the container has no options matching the efficiency target requirements - If
metric data is unavailable for all metrics in the selected efficiency target


VIEW RECOMMENDATIONS

Click a container name to view recommendation options and metrics for that
container. The details page shows current container’s compute and memory
requests, and the recommended requests for both.

The Metrics charts show current performance metrics and the target efficiency
range. You can use these charts to determine how your current usage compares to
the target metrics.

VIEW RECOMMENDATIONS BY NAMESPACE AND CLUSTER

The Rightsizing tool also gives you an option to view recommendations grouped by
Namespace and Cluster.

In the Group By dropdown, select Namespace and click Update. You will see all
the recommendations grouped by namespaces. Expand a namespace name to view all
the containers that fall into that specific account. Click the container name to
know the recommendation details of the selected container.



Select the Cluster option from the dropdown to group recommendations by clusters




SQL DB RIGHTSIZING

The new Rightsizing tool can help you use your SQL Databases (DB) more
efficiently and save money. By default, the Rightsizing tool provides the Best
Fit recommendation according to your business need with two additional
cost-effective DB recommendations.

Using Rightsizing, you can:

 * Analyze the workload patterns of your Virtual core (vCore) and Database
   transaction unit (DTU)-based purchasing model databases.
 * Apply custom efficiency targets to generate recommendations for optimal DB
   sizes and to meet different application and enterprise requirements
 * Gain clear insight into your total cost saving opportunities
 * Fine-tune DB sizing based on up to two additional recommendations.

Unlike the legacy Rightsizing tool, you can change efficiency targets and
instantly view updated recommendations to ensure the best fit.

The new Rightsizing tool provides recommendations for your vCore-based
(provisioned compute tier only) and DTU-based purchasing model.

To access our new Azure SQL DB Rightsizing tool, navigate to Recommendations >
Rightsizing.


USING THE RIGHTSIZING DASHBOARD

The Rightsizing dashboard provides information about the efficiency of your
databases, displays the status of each DB and provides recommendations for each
DB.

The Efficiency Summary provides the following information:

 * Current Recommendation: The total number of recommendations available in the
   dashboard.
 * Current Monthly Price: The sum of the monthly cost for all databases. Tanzu
   CloudHealth considers actual usage hours to date for the whole month to
   calculate the monthly price for each current DB using on-demand hourly rates
   for the current DB.
 * Projected Monthly Price: The monthly cost when the best fit is applied to all
   databases. If there are no recommendations, then the current DB’s hourly cost
   is used. Tanzu CloudHealth considers the same number of usage hours as the
   current DB and calculates the monthly price for all recommended databases
   using on-demand hourly rates for the recommended DB.
 * Projected Monthly Savings: The difference between Projected Monthly Cost and
   Current Monthly Cost. This is calculated by subtracting the Projected Monthly
   Savings from the Current Monthly Cost.

Click All Filters to view more filter options for your recommendations, such as
Subscriptions, Enrollments, Availability zone, Resource groups, and Perspective.

Select the required Purchase Model.

Click Export to download a CSV file of the recommendations.

You can set an Efficiency Target and Date Range to help generate targeted
recommendations. For more information about creating and using efficiency
targets, see Efficiency Targets.


UNDERSTAND RECOMMENDATIONS

The Recommendations table shows all databases that fit the selected filters and
that have a recommendation. You can also enable Show all recommendations to view
databases that do not currently have a recommendation.
The dashboard displays the number of databases with recommendations and their
fit:

 * Good fit: The DB meets all efficiency targets.
 * Under target: The DB is underutilized and does not meet all efficiency
   targets. Tanzu CloudHealth provides a recommendation to optimize the DB.
 * Over target: The DB is overutilized and exceeds one or more efficiency
   targets.
 * Undetermined fit: One or more of the metrics is above target, but one or more
   of another metric is below target, so the fit could not be determined.
 * Not enough data: There was no data available for a metric for the selected
   time period, so the fit could not be determined.
   

The Recommendations table also displays the following information:

 * Current Price: The current cost of the DB.
 * Recommended Price: The estimated cost of the DB after applying the most
   cost-effective recommendation.
 * Estimated Savings: The estimated monthly savings after applying the most
   cost-effective recommendation.

The prices for all databases are calculated using the list or on-demand prices
with no discounts applied, considering the actual usage hours of the database.


TYPES OF RECOMMENDATIONS

There are two types of recommendations:

 * Resize: Identifies the right size candidate DB that can handle the usage for
   the current DB. Resizing recommendations can be made across different DB
   types.
 * Terminate: Identifies databases that have less than one percent memory and
   CPU utilization, and can likely be terminated. If the connection success and
   failure rates are both zero, the Rightsizing tool will generate a termination
   recommendation. To check the connection rate value, you can refer to the
   Recommendation table, which displays the Connection Success Max and
   Connection Failure Max metrics.

If Tanzu CloudHealth is unable to make a recommendation for a DB, it is hidden
from the dashboard. You can enable Show all recommendations to view databases
that do not currently have a recommendation.

No recommendations are generated in the following situations:

 * Accelerated computing databases
 * If the DB has no options matching the efficiency target requirements
 * If metric data is unavailable for all metrics in the selected efficiency
   target


VIEW RECOMMENDATIONS

In the Rightsizing tool, you can view the recommendations by your purchasing
model. In the Purchase Model dropdown, select DTU-based or vCore-based
purchasing model.

Note that Tanzu CloudHealth does not provide recommendations for serverless
compute tier in the vCore-based purchasing model.



Click a DB name to view recommendation options and metrics for that DB. The
details page displays monthly cost for the DB, the primary recommendation and
provides up to two additional recommendation options.


The metrics charts show current performance metrics and the target efficiency
range. You can use these charts to determine how your current usage compares to
the target metrics.


VIEW RECOMMENDATIONS BY SUBSCRIPTION OR PERSPECTIVE

The Rightsizing tool also gives you an option to view recommendations grouped by
subscription or perspective.
In the Group By dropdown, select Subscription and click Update. You will see all
the recommendations grouped by subscription based on your initial purchasing
model selection. Expand a subscription name to view all the databases that fall
into that specific account. Click the DB name to know the recommendation
details.

For grouping by Perspective, you need to select a perspective group for which
you want to view the recommendations. Once you click Update, you will see all
the recommendations grouped by perspectives based on your initial purchasing
model selection. Expand a perspective name, and all the databases associated
with that specific perspective appear. Further, you can explore recommendations
details by clicking the DB name.




HOW THE NEW RIGHTSIZING TOOL MAKES VM RIGHTSIZING RECOMMENDATIONS

The following is an overview of how the rightsizing tool makes VM rightsizing
recommendations.


STEP 1: METRICS FOR CURRENT VM

Tanzu CloudHealth retrieves the following metrics for the selected date range to
determine how current VMs are being utilized:

 * CPU: CPU utilization scaled to vCPUs
 * Memory: memory utilization scaled to total available memory
 * Disk: disk utilization scaled to total attached storage


STEP 2: CANDIDATE LIST

The metric ranges for the applied efficiency target are then used to build a
list of VMs that are potential candidates for recommendations based on the
following criteria:

 * Only the VM attributes included in the applied efficiency target are
   considered. For example, if the applied efficiency target has ranges for CPU
   and Disk, the candidates for recommendations will be listed based on the
   evaluation of CPU and Disk metrics only. Any other available metrics are
   ignored.
 * The current utilization percentage for each metric is applied to VMs to
   calculate future utilization percentage assuming the same levels of usage.
 * The future utilization percentage of the VM type falls closer to the ranges
   specified in the applied efficiency target

If metrics for any of the current VM’s attributes are unavailable, Tanzu
CloudHealth ensures that the candidates have at least as much or more capacity
as that of the current VM.

Baseline performance thresholds for burstable VMs are used to evaluate the
current and future utilization percentage when the burstable VM is both a
current VM and a candidate for recommendations.


STEP 3: COMPARISON AND RECOMMENDATION

Candidates are removed if the source VM and candidate VM:

 * Have different architecture
 * Are from different regions

> If Recommend Burstables is checked, all burstable VM types are included in the
> candidate list.

The remaining candidates are sorted from least expensive to most expensive. The
least expensive candidate is the primary recommended VM type and is marked as
the Best Fit. Two additional VMs from the sorted candidate list are shown as
alternative recommendations.


HOW THE NEW RIGHTSIZING TOOL MAKES KUBERNETES RIGHTSIZING INSTANCE
RECOMMENDATIONS

The following is an overview of how the rightsizing tool makes EC2 rightsizing
recommendations.


STEP 1: METRICS FOR CURRENT CONTAINERS

Tanzu CloudHealth retrieves the following metrics for the selected date range to
determine how current containers are being utilized:

 * CPU: CPU utilization scaled to requested compute capacity
 * Memory: memory utilization scaled to requested memory capacity


STEP 2: CALCULATING RECOMMENDATIONS

The metric ranges for the applied efficiency target are then used to calculate
the ideal ranges or compute and memory requests so that the current usage will
fall within the performance ranges set in the applied Efficiency Target.

Only the container attributes included in the applied efficiency target are
considered.

For example, if the applied efficiency target has ranges for memory requests,
the calculations for recommendations will be based on the evaluation of memory
requests only. Any other available metrics are ignored.


HOW THE NEW RIGHTSIZING TOOL MAKES DB RIGHTSIZING RECOMMENDATIONS

The following is an overview of how the rightsizing tool makes DB rightsizing
recommendations.


STEP 1: METRICS FOR CURRENT DB

Tanzu CloudHealth retrieves the following metrics for the selected date range to
determine how current databases are being utilized:

For DTU based purchase model:

 * DTU- DTU utilization scaled to the DTU limit of a database.
 * Storage - Storage utilization scaled to Data Space used.
 * Connection_successful - scaled to Failed Connections.
 * Connection_failed -scaled to Successful Connections.

For vCore based purchase model:

 * CPU - CPU utilization scaled to CPU used by vCore instance..
 * Storage - Storage utilization scaled to Data Space used.
 * Connection_successful - scaled to Failed Connections.
 * Connection_failed -scaled to Successful Connections.


STEP 2: CALCULATING RECOMMENDATIONS

The metric ranges for the applied efficiency target are then used to build a
list of databases that are potential candidates for recommendations based on the
following criteria:

 * Only the DB attributes included in the applied efficiency target are
   considered. For example, if the applied efficiency target has ranges for DTU
   and Storage, the candidates for recommendations will be listed based on the
   evaluation of DTU and Storage metrics only. Any other available metrics are
   ignored.
 * The current utilization percentage for each metric is applied to databases to
   calculate future utilization percentage assuming the same levels of usage.
 * The future utilization percentage of the DB falls closer to the ranges
   specified in the applied efficiency target

If metrics for any of the current DB’s attributes are unavailable, Tanzu
CloudHealth ensures that the candidates have at least as much or more capacity
as that of the current DB.


STEP 3: COMPARISON AND RECOMMENDATION

Candidates are removed if the source and candidate databases are from different
regions. The remaining candidates are sorted from least expensive to most
expensive. The least expensive candidate is the primary recommended DB type and
is marked as the Best Fit. Two additional databases from the sorted candidate
list are shown as alternative recommendations.


EFFICIENCY TARGETS

Efficiency targets are a collection of a azure resource usage attributes and
utilization ranges that can be applied to meet application or enterprise
requirements. Tanzu CloudHealth’s new rightsizing report supports the following
Azure resources -

 * Azure VMs
 * Containers
 * SQL DB

Rightsizing includes two default efficiency targets: Average Metrics and Maximum
Metrics. You can also define your own targets by creating a custom efficiency
target.


CREATE A CUSTOM EFFICIENCY TARGET

To create a custom efficiency target:

 1. From the Actions menu, select Add custom efficiency target.
 2. Provide a name for your new efficiency target.
 3. Select one or more metrics to set as a target.
 4. Select whether the metric should be defined by average performance (AVG), or
    limit the metric from going beyond a maximum value (MAX).
 5. Define the target threshold range in values from 1 to 100.


VIEW, EDIT OR DELETE A CUSTOM EFFICIENCY TARGET

To view, edit, or delete an existing custom efficiency target:

 1. From the Actions menu, select Manage custom efficiency targets.
 2. Select the custom efficiency target from the Efficiency target dropdown.
 3. Make changes to the custom efficiency target:
    * To edit the custom efficiency target, modify any metrics and select Save.
    * To delete the custom efficiency target, select Delete.


ENABLE PUBLISHING VIRTUAL MACHINE METRICS TO AZURE MONITOR API

Using WAD table storage for collecting monitoring data from the guest OS may
result in a few missing metrics. Instead publishing virtual machine metrics to
Azure monitor API enables collecting the monitoring data of the guest OS from
the API. Installing and configuring this extension ensures no missing metrics.
It also enables the use of latest API version, resulting in fewer issues and
better support from the Microsoft support team.


RECOMMENDATIONS

The policy initiative configured in the following section can be assigned to a
management group at the organizational level. If there are no management groups
at the organizational level, except Tenant Root Group, follow the steps below:

 1. Log in to Azure Portal and search for Management groups.
 2. Search for subscriptions directly under the Tenant Root Group.
 3. If there are subscriptions directly under a Tenant Root Group, it is
    recommended to create a Management (Organizational) Group under the Tenant
    Root Group. This will route all subscriptions to the Organizational Group
    and then assign the policy initiative to the Organizational group.

> There will be no downtime while creating a Management Group.


CREATING POLICY INITIATIVE

CREATING A DATA COLLECTION RULE

 1.  Log in to Azure Portal.
 2.  Type monitor in the search bar or click on More Services and locate
     Monitor.
 3.  Under Monitor | Overview, search for Data Collection in the search bar.
 4.  Click on Data Collection Rules and click on Create. Fill in the details and
     pick any Subscription/Resource group where the Data Collection Rule will be
     stored. Under the Platform Type, select Windows or Linux.
 5.  Navigate to the Collect and Deliver tab and click on Add data source.
 6.  For Data source type, select Performance counters from the dropdown. Verify
     that all Basic and Custom performance counters are selected and click on
     Next: Destination.
 7.  Select Azure Monitor Metrics (preview) and click on Add data source.
 8.  Click on Next: Review + create > and navigate to the Review + create tab.
 9.  Click on create to create the data collection rule. Validate the rule
     collection by verifying the Validation passed message.
 10. The data collection rule has been successfully created and deployed.

FETCHING A RESOURCE ID OF THE DATA COLLECTIVE RULE

 1. Navigate to Monitor | Overview and locate the new data collection rule that
    was created and click on JSON View.
 2. In the panel, locate the resource ID of the data collection rule. Copy this
    to the clipboard. This resource ID will be used later while creating the
    policy initiative.

AZURE BUILT-IN POLICIES

Add the following Azure in-built policies while creating the initiative:


For Windows


 1. Assign Built-In User-Assigned Managed Identity to Virtual Machines.
 2. Assign Built-In User-Assigned Managed Identity to Virtual Machine Scale
    Sets.
 3. Configure Windows Virtual Machines to run Azure Monitor Agent with
    user-assigned managed identity-based authentication.
 4. Configure Windows Virtual Machine scale sets to run Azure Monitor Agent with
    user-assigned managed identity-based authentication.
 5. Configure Windows machines to be associated with a Data Collection Rule.

For Linux


 1. Assign Built-In User-Assigned Managed Identity to Virtual Machines.
 2. Assign Built-In User-Assigned Managed Identity to Virtual Machine Scale
    Sets.
 3. Configure Linux Virtual Machines to run Azure Monitor Agent with
    user-assigned managed identity-based authentication.
 4. Configure Linux Virtual Machine scale sets to run Azure Monitor Agent with
    user-assigned managed identity-based authentication.
 5. Configure Linux machines to be associated with a Data Collection Rule.

CREATING A POLICY INITIATIVE

The policy initiative can be scoped to a subscription. However, if a management
group exists at the tenant level, then it can be added to the group.

 1.  Search for Policy and click on it.
 2.  Click on Definitions to define an initiative. Click on Initiative
     definition button to create an initiative.
 3.  Navigate to the Basics tab and enter the basic information. Pick any
     subscription/management group, where the policy initiative is to be stored.
     Click on Next or navigate to the Policies tab.
 4.  To add policies, click on Add Policy definition(s) which will open a new
     pane.
 5.  Select Built-in from the drop down. Search for and add each policy
     mentioned at Azure Built-in Policies.
 6.  Select all the policies and click on Add selected policies to a group. Use
     a Management group if it exists at the root level.
 7.  Navigate to Initiative parameters tab.
 8.  Click on Create Initiative parameter to create a parameter for data
     collection rule. Select the Type as String. Use the data collection rule
     resource ID and click on Save.
 9.  Add an additional initiative parameter for user-assigned identity. Select
     the Type as Boolean and retain the default value as False. Click on Save.
 10. Navigate to the Policy parameters tab.
 11. Locate the Parameter name related to the data collection rule ID and set
     the value as Use Initiative Parameter. Use the name given to the Data
     collection Rule Resource ID while creating parameters.
 12. For others, select the VALUE TYPE as Use Initiative Parameter and choose a
     value from the drop down. For instance, Bring Your Own User-Assigned
     Managed Identity.
 13. Verify all the configuration at the Initiative Definition tab.
 14. Navigate to the Review + Create tab and click on Create.
 15. Under the Policy page, navigate to Definitions tab to view the policy
     initiative created and click on Assign. Choose from the dropdown for scope
     and click on Review + Create.

ASSIGNING POLICY INITIATIVE TO ANOTHER SCOPE

 1. Navigate to Policy page and click on Assignments. Select the policy
    initiative to be assigned.
 2. Click on Assign to another scope.
 3. Click on Scope and select a subscription/management group from the drop
    down. Click on Select.
 4. Click on Review + create and click on Create.
 5. Allow about 30 minutes for the assignment. On assigning successfully, the
    following notification will be displayed:
 6. Navigate to View definition tab to view the new assignment.
 7. Navigate to Assignments to view all the scopes that the policy initiative
    has been assigned to.
 8. Follow the same process to assign the initiative policy to more
    scopes/management groups.


REMEDIATION TASKS

Remediation is required if there are existing resources in the subscription.

 1. Navigate to Policy | Assignments and click on the policy initiative created.
 2. Navigate to Create Remediation Task tab and select one policy to remediate.
    Select the check-box for Re-evaluate resource compliance before remediating
    and click on Remediate.
 3. On creating a remediation task, it will be in the Evaluating state. Create
    tasks for all policies. Once the remediation is completed, the status will
    change from Evaluating to Complete.


ADDITIONAL NOTES

 * Find the list of OS supported by the Azure Monitor Agent at Overview of the
   Azure monitoring agents - Azure Monitor.
 * On switching to Azure Monitor Agent, expect a turnaround time of about 60
   minutes for the metrics to show up on the portal and to be fetched via the
   API.
 * The steps documented are required to fetch guest OS metrics from the Monitor
   API version 2018-01-01.

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In this article

Azure VM Rightsizing

Using the Rightsizing Dashboard Understand Recommendations Types of
Recommendations View Recommendations Kubernetes Rightsizing

Using the Rightsizing Dashboard Understand Recommendations Resize
Recommendations View Recommendations SQL DB Rightsizing

Using the Rightsizing Dashboard Understand Recommendations Types of
Recommendations View Recommendations View Recommendations by Subscription or
Perspective How the New Rightsizing Tool Makes VM Rightsizing Recommendations

Step 1: Metrics for Current VM Step 2: Candidate List Step 3: Comparison and
Recommendation How the New Rightsizing Tool Makes Kubernetes Rightsizing
Instance Recommendations

Step 1: Metrics for Current containers Step 2: Calculating Recommendations How
the New Rightsizing Tool Makes DB Rightsizing Recommendations

Step 1: Metrics for Current DB Step 2: Calculating Recommendations Step 3:
Comparison and Recommendation Efficiency Targets

Create a Custom Efficiency Target View, Edit or Delete a Custom Efficiency
Target Enable Publishing Virtual Machine Metrics to Azure Monitor API

Recommendations Creating Policy Initiative Remediation Tasks Additional Notes
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