Recommendations on StormForge Documentation

Requests and limits

Mon, 01 Jan 0001 00:00:00 +0000

Every StormForge recommendation includes CPU and memory requests that the machine learning has determined to be optimal for each container in a workload.

Optimize Live generates CPU and memory recommendations using our patent pending machine learning. Our machine learning examines each container’s resource usage metrics, and monitors usage patterns and scaling behavior to come up with the optimal CPU and memory resource settings.

Requests

Request recommendations for CPU and memory are based on observed resource usage, constrained by optimization settings. After the machine learning generates recommendation candidates, an effective recommendation is created by:

Java heap size

Mon, 01 Jan 0001 00:00:00 +0000

StormForge recommendations can optionally include the Java max heap size that the machine learning has determined to be optimal for each Java container in a workload.

When configured, Optimize Live automatically analyzes critical Java metrics—such as heap and non-heap usage, as well as garbage collection data—and provides tailored recommendations for heap size adjustments alongside its recommendations for the container’s requests and limits.

Enabling heap size recommendations

To create Java Heap size recommendations, the StormForge Agent must be configured to collect JMX metrics. Use the jvmWorkloadConfigs Helm parameter to accomplish this.

Pod scheduling

Mon, 01 Jan 0001 00:00:00 +0000

StormForge recommendations can optionally include node affinity, hard or soft, that influences the scheduler to place workloads on specific kinds of nodes.

The goal of this feature is to provide better node utilization and bin packing by placing workloads on nodes that match their CPU and memory characteristics. Optimize Live categorizes every workload according to its CPU:Memory request ratio, then adds node affinity for node types with similar CPU:Memory resource ratios.

Generate recommendations on demand

Mon, 01 Jan 0001 00:00:00 +0000

This feature is helpful when you are experimenting with settings and want to see the results as you go.

In the left navigation, click Optimize Live > Workloads, then find and click the name of the workload you want to work with.
On the workload details page, in the Recommendation section of the page header, click Regenerate.
Optional: To apply the recommendation, complete one of the following steps:
- Click Apply Now.
- Apply the recommendation as patch: On the Patches tab of the workload details page, download the patch and then copy and run the generated kubectl apply command.
- Run stormforge apply --name WORKLOAD_NAME from the command line, replacing WORKLOAD_NAME with the name of the workload. (Note: The optional StormForge CLI tool must be installed.)

View recommendations

Mon, 01 Jan 0001 00:00:00 +0000

A recommendation includes the CPU and memory requests and limits values (and recommended HPA configuration, if an HPA is running) that the machine learning has determined to be optimal for each container in a workload.

By default:

Recommendations are not applied automatically, giving you the opportunity to review their projected impact on your workloads. You can configure Optimize Live to deploy recommendations automatically, as described later in this topic.
Recommendations are applied as patches. You can choose to configure Optimize Live to use a mutating admission webhook to apply recommendations, as described later in this topic.

Viewing the overall projected impact of a recommendation

To understand how a recommendation will right-size a workload, review the workload details page header:

Apply recommendations

Mon, 01 Jan 0001 00:00:00 +0000

During a workload’s learning period, the preliminary recommendations generated by Optimize Live can be applied manually only.

After the learning period, when you’re satisfied with how the generated recommendations can right-size your workloads, you can deploy them in any of the ways described below.

Choosing an apply method - patch or webhook?

By default Optimize Live applies recommendations as patches to a workload.

You can choose to configure Optimize Live to apply recommendations directly to Pods via a mutating admission webhook. You might choose a webhook method if you use third-party tools to monitor for configuration drift or if you have many custom resources.

Recommendations on StormForge Documentation

Requests and limits

Requests

Java heap size

Enabling heap size recommendations

Pod scheduling

Generate recommendations on demand

Related topics

Installation > Advanced installation

View recommendations

Viewing the overall projected impact of a recommendation

Apply recommendations

Choosing an apply method - patch or webhook?