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Version: 1.7

Resource Recommender

Introduction ​

Resource Recommender is a Devtron feature that analyzes the history of resource usage (such as CPU and memory) for Kubernetes workloads in a specific cluster and generates data-backed recommendations for optimal resource allocation.

These recommendations are generated by a background job that runs daily at a specific time by default, using resource usage metrics collected from Prometheus. Job frequency can be customized by modifying the configuration in the ConfigMap. Refer to Modify Resource Recommender Job Schedule to learn more.

It helps users eliminate manual guesswork, reduce over-provisioning, and prevent performance issues caused by under-provisioned workloads.

Prerequisites
Who Can Perform This Action?

To view recommendations, users need to have view permissions for the specific cluster.

To apply recommendations, users need to have admin permissions for the specific cluster.

Inspect Recommendations​

To inspect the resource recommendations, follow the steps below:

  1. Navigate to Resource Browser.

    Figure 1: Navigating to Resource Browser

  2. Select the specific cluster for which you want to inspect the resource recommendations.

    Figure 2: Selecting Cluster

  3. Select the Resource Recommender tab to inspect the recommendations.

    Figure 3: Selecting Resource Recommender

    1. Each row displays workloads in that cluster, along with recommendations for CPU and memory.

      Figure 4: Displaying Workloads along with Recommendations

    2. Recommendations are shown as percentage changes by default. You can use the Show absolute values checkbox to see current v/s recommended values in actual units (e.g., 128Mi → 4900Ki).

      Figure 5: Toggling 'Show Absolute Values'

info

No Recommendations​

  • In case you have just configured the Prometheus endpoint, you might face the No recommendations yet screen after selecting Resource Recommender.

  • Click Check for Recommendations to initiate the scan, and recommendations will be available in a few minutes.

  1. You can inspect the resource recommendations for each workload within the cluster.

    1. If you wish, you can filter the workloads based on their Kind and Namespace.

      Figure 6a: Filtering via 'Kind'

      Figure 6b: Filtering via 'Namespace'

    2. You can also search for a specific workload via the search bar based on WORKLOAD, NAMESPACE, KIND, API VERSION, and CONTAINER NAME.

      Figure 7: Filtering via Search Bar

  2. For each workload, recommendations can be of the following types:

Types of Recommendation​

TypeVisual IndicatorDescription
↑Red upward arrowIndicates that the current resource allocation is below the recommended threshold, and more resources should be provisioned to ensure successful builds.
↓Green downward arrowIndicates that the current resource allocation is above the recommended requirement, and resources can be safely reduced to optimize cost and efficiency.
0% -Gray dash (-)Indicates that the current allocation matches the recommended value, and no changes are needed.
No RecommendationNo RecommendationNo recommendation available

Supported Resources​

TypeDescription
CPU REQUESTSCPU guaranteed to the workload
CPU LIMITSMaximum CPU allocatable to the workload, if available
MEM REQUESTSMemory (RAM) guaranteed to the workload
MEM LIMITSMaximum Memory (RAM) allocatable to the workload, if available

  1. Hover the cursor over the specific resource of the particular workload to view the recommendation.

    Figure 8a: Recommending for Increasing Resources

    Figure 8b: Recommending for Decreasing Resources

    Figure 8c: Recommending to not take any action

    Figure 8d: No Recommendation

Rescan for Recommendations​

If you want to rescan for recommendations, use this option to fetch the most recent data for your cluster. To do so, follow the steps below:

  1. Select the kebab menu (â‹®) from the right side.

  2. Click Rescan for recommendations.

  3. Rescanning will start, and recommendations will be updated in few minutes.

    Figure 9: Rescanning for Recommendations

Apply Recommendations​

After inspecting the recommendations, you can directly apply them to your workloads in the following ways:

Apply Per Workload​

To apply recommended resources to a specific workload, follow the steps below:

  1. Hover over the specific workload row and click the Apply button that appears on hover.

    Figure 10: Selecting Workload

  2. A modal window will open, displaying the comparison between the current and recommended resource values for that specific workload; review the recommended changes carefully.

    Figure 11: Comparing Configurations

  3. Click Apply Changes to update the resource configuration.

    Figure 12: Applying Changes

Bulk Apply​

If you wish, you can apply recommended resource configurations to multiple or all workloads at once. To do so, follow the steps below:

  1. Select the workloads for which you want to apply recommended resource configurations and click Apply Changes.

    Figure 13: Selecting Workloads

  2. A modal window will open, displaying the comparison between the current and recommended resource values for each workload; review the recommended changes for each workload carefully.

    Figure 14: Comparing Configurations for each workload

  3. Click Apply Changes to update the resource configuration.

    Figure 15: Applying Changes

Export Recommendations in CSV File​

You can also export resource recommendations to a CSV file. To do so, follow the steps below:

  1. Filter the workloads for which you want to export resource recommendations, based on their Kind and Namespace. By default, it will export resource recommendations for all workloads.

  2. Click the Download button; a dialog box will appear displaying that your export is ready. By default, the file will be downloaded automatically to your system. If you wish you can select Click Here To Download Manually to download the file manually.

    Figure 16a: Clicking Download Button

    Figure 16b: Export Ready

Modify Resource Recommender Job Schedule​

To modify the schedule of the Resource Recommender background job, follow the steps below:

  1. In a new tab, go to Resource Browser → (Select the Cluster for which you want to modify the resource recommender job schedule) → Config & Storage → ConfigMap

  2. Select the Orchestrator/Devtron ConfigMap

    1. If Devtron is managing your setup, contact Devtron support to perform these changes.

    2. If you installed Devtron via Helm, select devtron-cm and click Edit Live Manifest.

    Figure 17a: Selecting Devtron CM

    Figure 17b: Clicking 'Edit Live Manifest'

  3. Edit the KRR_SYNC_JOB_CRON_SCHEDULE key and set the schedule according to your use case using a cron expression.

  4. Click Apply Changes and manifest will be updated.

    Figure 18: Applying Changes

  5. Restart the orchestrator or devtron pod to implement the changes.

    Figure 19: Restarting Pods