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To produce recommendations for workloads, the StormForge Agent needs to be installed on the Kubernetes cluster where the workloads are deployed to and then registered with the StormForge Optimize platform.
A workload is a component that runs inside one or more Kubernetes pods. In the context of Optimize Live, a workload is a named Workload resource from a namespace in a cluster. Optimize Live observes the resource utilization of workloads in order to produce recommendations for them.
Optimize Live can produce recommendations for the following Workload types: DaemonSet, Deployment, Pod, ReplicaSet, ReplicationController, and StatefulSet.
To minimize the footprint in a cluster, Optimize Live requires only the StormForge Agent to be installed on a cluster. By default, the Agent is installed in the
The StormForge Applier patches a cluster’s workloads with the recommended resource utilization values generated by the Optimize Live machine learning. The Applier uses the same credentials file as the Agent and runs in the same namespace.
Install the Applier if you plan to:
- Deploy recommendations automatically on a schedule of your choosing. This option enables you to skip manually reviewing the recommended settings and ensures your settings track closely to actual CPU and memory use.
- Deploy recommendations on demand. For example, you can apply a single recommendation in any environment as you experiment with recommendations or if you need to quickly deploy a recommendation outside of a schedule.
An Optimize Live recommendation is the set of resource requests and limits that the machine learning algorithm has determined to be optimal for a workload, based on historical utilization observations.
It typically takes about 7 days’ worth of metrics to generate a recommendation that you can apply, often referred to as a complete recommendation.
During this intial 7-day metrics collection period, you can view preliminary recommendations based on the metrics collected so far:
- One hour after installation, you’ll see the first preliminary recommendation based on the metrics collected so far. You can view preliminary recommendations, but you should not apply them, because they’re not based on a complete set of metrics.
- For hours 2 to 24 after installation, metrics collection continues and preliminary recommendations are generated hourly. You might notice the recommendations becoming more refined.
- On day 2 to 7 after installation, metrics collection continues and preliminary recommendations are generated once daily.
- On day 7, complete recommendations are available to apply, and continue to be generated on the schedule of your choosing (or once daily by default if you don’t set a schedule).
For details about viewing applying recommendations, see View and apply recommendations.