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Overview

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After you enable CCM, it may take up to 48 hours for the recommendations to appear in Cloud Costs. It depends on the time at which CCM receives the utilization data for the service. In Cloud Costs, go to the Recommendations page.

Harness CCM currently supports these types of recommendations:

  • Azure VM
  • AWS EC2 instances
  • AWS ECS services
  • Nodepool
  • Workload
  • Governance

View recommendations​

To view recommendations, click on the Recommendations tab in the navigation bar, which will take you to the homepage. On the Recommendations page, there are two tabs: Open Recommendations and Applied Recommendations. The Open Recommendations tab displays all available recommendations that have yet to be applied, while the Applied Recommendations tab shows the recommendations that have already been implemented.

Open Recommendations​

Applied Recommendations​

When you click on an individual recommendation, you’ll be able to view a detailed breakdown of the recommendation, including relevant insights, suggested actions, and any supporting information.

Azure VM Recommendations​

A highly effective way to reduce Azure VM costs is by optimizing VM utilization. By clicking on the Azure VM recommendation, you can view detailed information, as shown below:

AWS EC2 Recommendations​

AWS ECS Recommendations​

Nodepool Recommendations​

Workload Recommendations​

Governance Recommendations​

Please see the links below for details about Governance Recommendations:

Recommendation Settings​

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New Recommendation Preferences may take up to 24 hours to fully update across the platform. However, changes will be reflected immediately on the drill-down page, while the Overview page may take additional time to reflect updates.

Recommendation settings allow users to customize how recommendations are generated and displayed within the platform. The recommendation settings are divided into two main sections: Preferences and Manage Presets.

Click on "Settings" to view/edit the preferences and presets.

Preferences​

General Preferences:​

  • Show Recommendations on Parent Resources - Enables recommendations for parent-level resources such as Nodepool, EC2, and ECS Services. This ensures users receive optimization suggestions for high-level infrastructure components.

  • Show Recommendations on Child Resources - Displays recommendations for individual workloads, allowing users to optimize specific application components rather than just the underlying infrastructure.

  • Show Recommendations on Resources Added to the Ignore List - If enabled, recommendations will still be displayed for resources that have been manually marked as ignored.

Resource Specific Preferences:​

Over here, users can select the presets for each resource type and also set the default time range.

Manage Presets​

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By default, Harness CCM has default presets for all resources but users can tune recommendations using custom values. To set custom values, click on the recommendation and expand the "Tune Recommendations" section to configure the tuning parameters.

This helps users to create and save customized configurations for their recommendations. These presets capture specific user preferences, such as tuning parameters for resource types like workloads, nodepools, ECS, and EC2 instances.

Key Benefits:

  • Time-Saving Automation - There is no longer any requirement to manually adjust settings each time recommendations are generated, reducing repetitive work.

  • Flexibility and Customization - Different presets can be created for various use cases, and users can apply the most suitable settings as needed.

  • Simplified User Experience - Managing preferences through presets enhances usability, making it easier to navigate and control recommendation settings.

Users can fine-tune recommendations for different resource types by configuring specific tuning parameters and save presets.

Resource TypeTuning Parameters supported in Presets
WorkloadQuality of Service (QoS), Percentage Buffer for CPU/Memory
NodepoolMinimum Node Count, Percentage Buffer for CPU/Memory
AWS EC2Show recommendation within the same instance family or across instance families
AWS ECSBuffer Percentage

K8s Workload​

  • Quality of Service (QoS)
  • Percentage Buffer for CPU/Memory

K8s Nodepool​

For nodepools, users can set the following parameters:

  • Minimum Node Count

  • Percentage Buffer for CPU/Memory

Amazon ECS​

For ECS workloads, users can configure:

  • Percentage Buffer for CPU/Memory

Amazon EC2​

For EC2 instances, users can choose how recommendations are displayed:

  • Within the Same Instance Family: Recommendations will suggest optimized instance types within the same instance family, ensuring workload compatibility and minimizing migration complexity.

  • Across Instance Families: Recommendations can suggest optimized instance types across different instance families, potentially unlocking greater cost savings and efficiency improvements.

Ignore list​

You can put any EC2 instance, VM, Service, Nodepool, Workload, Governance recommendation in Ignored list. Adding resources to the Ignore list will stop Harness from displaying recommendations for those resources. You can view the Ignore list with details by clicking on "Manage Ignore List" on the overview page.

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For Governance, we support Granular Recommendations. Owing to this, now, while adding a recommendation to Ignore List, users have the option to specify the scope at which the users want to ignore the recommendation.

To remove a recommendation from the ignore list, select Manage Ignore List. Then, select Remove.

Apply recommendations​

To apply recommendations, you need to raise a ticket in the ticketing tool that your organization uses. To configure the ticketing tool setup, perform the following steps:

  1. Navigate to Cloud Costs > Setup > Default Settings.
  2. Expand Cloud Cost Management.
  3. Under Ticketing preferences, select the Ticketing tool and the Ticketing tool connector. If you do not have an existing connector, create a new one.

The default ticketing tool is Jira. You can choose ServiceNow if that's the tool used in your organization.

  1. Select Save.
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Switching your ticketing tool between Jira and ServiceNow results in the removal of the existing recommendation tickets. The status of the tickets changes to Create a ticket.

Go to the Recommendations page and create tickets to apply recommendations.

Perform the following steps to raise a ticket to apply recommendations:

  1. Select Create a ticket. In case you haven't set up your ticketing tool settings on the account level, you will see a prompt guiding you to access the Default Settings page to configure both the ticketing tool and the associated connector.

  2. Enter the following ticket details:

  • Jira project — Select the Jira project where you want to create a ticket. Go to Create Jira Issues in CD Stages.
  • Issue type — Select a Jira issue type from the list of types in the Jira project you selected. Go to Create Jira Issues in CD Stages.
  • Ticket summary — Add a summary of the issue.
  • Description — Add a description for the issue.

The ticket is created. The status of the ticket changes to To do. You need to assign the ticket to apply the recommendations. After applying the recommendations, when the ticket status changes to Done, the recommendation is displayed in the Applied Recommendations tab. If you have not changed the ticket status to Done after applying the recommendation, you could use the More actions icon (three vertical dots), and then select Mark as applied to move the recommendation to the Applied Recommendations tab. To view the recommendations that are applied, and the cost savings realized, select the Applied Recommendations tab.

Create a Jira or ServiceNow connector​

  1. In the Overview step, enter the name.
  2. Optionally, enter a short description and tags.
  3. In the Details step, enter the base URL. For example, https://mycompany.atlassian.net. For more information, go to Configuring the base URL.
  4. Enter the email ID used to log in to your Jira account.
  5. Create or select a Harness encrypted test secret for the API key. To create a secret, go to Add and reference text secrets.
  6. In the Delegates Setup step, select one of the following options:

    • Use any available Delegate: Select this option to let Harness choose a Delegate at runtime. When a task is ready to be assigned, the Harness Manager first validates its lists of Delegates to assign the task. The following information describes how the Harness Manager validates and assigns tasks to a Delegate:

      • Heartbeats: Running Delegates send heartbeats to the Harness Manager in one-minute intervals. If the Manager does not have a heartbeat for a Delegate when a task is ready to be assigned, it will not assign the task to that Delegate.

      • Tags: Go to Use delegate selectors.

      • Allowlisting: After a Delegate is validated for a task, it is allowlisted for that task. This Delegate is likely to be used again for that task. The allowlisting criteria is the URL associated with the task, such as a connection to a cloud platform, repo, or API. A Delegate is allowlisted for all tasks using that URL. The Time-To-Live (TTL) for the allowlisting is 6 hours, and the TTL is reset with each successful task validation.

      • Blocklisting: If a Delegate fails to perform a task, it is blocklisted for that task. This Delegate is not selected again to perform that task. TTL is five minutes. This is true even if there is only one Delegate and the Delegate is selected for that task with a Selector.

    • Only use Delegates with all the following tags: Select this option to use specific Delegates using their Tags. You only need to select one of a Delegate's Tags to select it. All Delegates with the Tag are selected. This option is useful when you have installed specific software on specific Delegates and want to ensure that your Pipeline uses those Delegates.

  7. Select Save and Continue. The Connector is created and validated.

You can create and use filters to select resources and recommendations.

You can filter by:

  • Resource Name — The name of the resource being monitored.
  • Namespace — A namespace in the cluster.
  • Cluster Name — A cluster in your infrastructure.
  • Resource Type — The type of resources for which the recommendation is displayed. Currently, CCM supports ECS service, node pool, and workload.
  • Savings — Enter the minimum monthly savings. For example, all the recommendations with potential monthly savings of more than $1000.
  • Potential Spend — Filter by forecasted monthly spend greater than the specified amount. For example, all the recommendations with forecasted monthly spend of more than $1000.

The recommendation for the selected resource is displayed.

Select a recommendation to view its details.

Within a recommendation, select the number of days to compute recommendations based on the utilization data. You can select the last day, 7 days, or 30 days.

You can use this information to optimize your resources to potentially reduce your monthly cloud costs.

ServiceNow ticket​

The Description field contains relevant information about the recommendation for which this ticket was created. Harness CCM retrieves the following data from ServiceNow:

When a user opens the dialog box to create a ServiceNow ticket, a request is made to obtain all possible ticket types. Here is a sample response:

{
    "status": "SUCCESS",
    "data": [
        {
            "key": "asset_reclamation_request",
            "name": "Asset Reclamation Request"
        },
        {
            "key": "asset_task",
            "name": "Asset Task"
        },
        {
            "key": "business_app_request",
            "name": "Business Application Request"
        }
    ]
}

Once the user selects a ticket type, another request retrieves the fields associated with that ticket type to determine the required fields. Here is a sample response:

{
    "data": [
        {
            "key": "parent",
            "name": "Parent",
            "required": false,
            "schema": {
                "array": false,
                "typeStr": "",
                "type": "unknown",
                "customType": null,
                "multilineText": false
            },
            "internalType": null,
            "allowedValues": [],
            "readOnly": false,
            "custom": false
        },
        {
            "key": "made_sla",
            "name": "Made SLA",
            "required": false,
            "schema": {
                "array": false,
                "typeStr": "boolean",
                "type": "boolean",
                "customType": null,
                "multilineText": false
            },
            "internalType": null,
            "allowedValues": [],
            "readOnly": false,
            "custom": false
        }
    ]
}

When the user clicks "Create Ticket," an API call is made to ServiceNow to create the ticket with the provided inputs. Additionally, there is an internal call that periodically checks if the ticket has been closed. Based on this status, the recommendation is moved to the applied state.

Export recommendations data​

You can export your Recommendations as comma-separated values (CSV) files. Exporting allows you to use the data in other software. Export respects the filters applied by the user in the filter panel.

Limitations​

  • Only comma-separated values files (CSV) are supported.
  • The maximum number of rows allowed in one export is 10,000 rows.
  • The more rows you export, the slower the export will be.

Create a Recommendations Export​

  1. Open Recommendations.

2. Click Export CSV.

  1. Enter a name for the CSV file.

  2. In Export rows up to, enter the number of rows you want exported. The number of rows should be greater than or equal to 1.

  3. Click Download. The file is downloaded to your computer.

Recommendations Filtering Support​

Harness provides filtering support for recommendations based on cloud account identifiers and Kubernetes attributes. This allows for better cost optimization insights while maintaining alignment with perspective-based RBAC settings.

Filtering Support for Recommendations​

  • AWS EC2: Filtering is supported on AWS Account ID. Nested Cost Categories are not supported.

  • AWS ECS: Filtering is supported on AWS Account ID. Nested Cost Categories are not supported.

  • Azure VM: Filtering is not supported.

  • Kubernetes: Filtering is supported on Labels and Cluster Name. Nested Cost Categories are not supported.

  • Governance Recommendations:

    • AWS: No filtering support.
    • Azure: No filtering support.
    • GCP: No filtering support.
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Filtering support for recommendations extends to RBAC configurations based on perspective folder access settings, ensuring that cost-saving suggestions are appropriately scoped to the right teams.