Workload Autoscaler Installation

This document provides a guide to help you install the CloudPilot AI Workload Autoscaler component.

Prometheus Requirement

The Workload Autoscaler requires Prometheus as a data source to retrieve cluster resource usage, such as container CPU usage and CPU requests.

If you haven’t deployed Prometheus yet, you can install it using the following command. When installed via the Helm Chart, Prometheus will automatically collect all the required data by default.

# Configure the storage class for Prometheus, if you have a default storage class, you can leave it empty
export STORAGE_CLASS=""
 
kubectl create namespace prometheus
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
 
helm upgrade -i prometheus prometheus-community/prometheus \
    --namespace prometheus \
    --set alertmanager.persistence.storageClass="${STORAGE_CLASS}" \
    --set server.persistentVolume.storageClass="${STORAGE_CLASS}"

You can then use the following address as the Prometheus endpoint: http://prometheus-server.prometheus.svc

If you have already deployed Prometheus, you need to run the following PromQL to verify that Prometheus is correctly collecting the metrics required by CloudPilot AI.

# Usage datas is collected from the kubelet-cadvisor.
rate(container_cpu_usage_seconds_total{
  container!="", container!="POD",
  namespace="kube-system",
  pod=~"coredns.*"
}[5m])

# Request and Limit datas are collected from the kube-state-metrics component.
kube_pod_container_resource_requests{
  resource="cpu",
  container!="",
  namespace="kube-system",
  pod=~"coredns.*"
}

If Prometheus is deployed but not collecting the required metrics, you can contact our technical support team for assistance with the deployment process.

Installation

Once Prometheus and the corresponding data sources are ready, you can begin deploying the Workload Autoscaler component from the Workloads page.

If you are unsure of your Prometheus endpoint, please reach out to our technical support team for guidance.

Note: By default, after deployment the Workload Autoscaler will automatically calculate recommendations for certain workloads but will not apply updates. Once the deployment is complete, you can configure a AutoscalingPolicy to adjust workload recommendations and update strategies.

Upgrade and Uninstall

The Workload Autoscaler component is upgraded together with the CloudPilot AI version—you do not need to upgrade it separately.

Similarly, the Workload Autoscaler component is uninstalled together with the CloudPilot AI version. If you need to uninstall the Workload Autoscaler component independently, please contact our technical support team for assistance.

Note: Before uninstalling the Workload Autoscaler, please make sure that all AutoscalingPolicies have been deleted or disabled, and confirm that all Workloads have been restored to their original state.