Rightsizing recommendations and Workload Autoscaling

How long does it take to start generating enough data to create useful recommendations?

30 minutes is enough for Cast AI to start creating valuable recommendations.

Does our automatic workload rightsizing take HPA into account?

Yes, it does.

With Workload Autoscaler, is the castai-agent VPA still needed?

Currently, it's still needed. The agent is a special case for Workload Autoscaler since this is where it gets its metrics. If the agent dies, woop is 'blind', while for any other workload in the cluster, woop would eventually upscale it based on incoming metrics.

Do we have a way to support Workload Autoscaler declaratively on the workload and namespace level ?

Currently, Cast AI doesn't support this.

What does red "!" mean on the workload autoscaler page?

The error usually occurs when the Cast AI workload autoscaler fails to perform an action during workload optimization, often due to an unresponsive cluster controller.

What time range does the workload autoscaler use for its recommendations?

The workload autoscaler bases its recommendations on metrics collected over 24 hours by default, although this can be configured individually. It continually observes pod metrics exposed by the metrics-server to generate recommendations.

Does the workload autoscaler allow configuring weights for specific days in its recommendations?

Currently, the workload autoscaler does not allow you to set weights for certain days or exclude weekends. It continuously gathers metrics and generates recommendations based on historical usage over the past 24 hours (default) or as configured (up to 7 days).

What happens when you turn off workload autoscaler on a workload?

The workload autoscaler will revert to the old requests set in the deployment manifest.

Which logs are available for the workload autoscaler, and how can we verify that it functions properly for an enabled application?

You can check the event logs for any recent entries related to WOOP operations or errors. Additionally, you can review the logs from the workload-autoscaler pod to observe WOOP’s performance and any issues it may be encountering during its operation.

What is the impact of annotations on UI configuration settings?

When annotations are applied to a workload, the corresponding UI configuration settings are overriden. If someone attempts to update the UI after annotations have been applied, those UI changes will not take effect.

Does Woop process applications differently on Spot Instances versus on-demand instances?

No, WOOP processes applications similarly on spot and on-demand instances.

Can Workload Autoscaler be applied to workloads running on Auto Scaling Group (ASG) nodes?

Yes, it can be applied to applications running on ASG nodes.

How frequently can Workload Autoscaler change a recommendation within an hour?

Workload Autoscaler can change a recommendation up to 240 times per hour, approximately every 30 seconds.

How long will a CPU run high before it is scaled up?

Under normal conditions, scaling up occurs every 30 minutes. During a surge (CPU above percentile settings, OOM, change settings), scaling happens immediately, subject to the Optimization Threshold.

What happens if Workload Autoscaler detects an Out Of Memory (OOM) condition?

When an OOM condition is detected, Woop immediately increases the memory overhead by a fixed amount.

Does WOOP support sidecar pods?

Yes.