Automation setup instruction — Self-hosted 2.0
Automation is currently available for workloads with type Deployment, DaemonSet, StatefulSet, CronJob and Job.
PerfectScale does not support automating workloads that use the OnDelete update strategy. The OnDelete strategy requires manual intervention to update pods, which falls outside the scope of PerfectScale's automated optimization capabilities.
Step 1: Install PerfectScale Automation Agent
When deploying the PerfectScale Automation Agent in a GKE Private Cluster, you need to create an additional firewall rule. This enables the Control Plane address range to communicate with the Cluster Pod IPv4 address range on port 8443. If the webhook call fails, see the troubleshooting guide.
Deploy the PerfectScale automation agent. In order to install the PerfectScale automation agent, run the following commands:
helm repo add perfectscale https://perfectscale-io.github.io --force-update
and
helm upgrade --install -n perfectscale psc-autoscaler \
--set secret.create=false \
--set settings.psUrl=<https://api-{your-web-ui-url}> \
--set settings.telemetryUrl=<https://api-{{your-web-ui-url}}/psc-telemetry> \
perfectscale/psc-autoscaler
If taints and tolerations are defined in the node pool, you must include them in the command too, so it will appear like this:
helm upgrade --install -n perfectscale psc-autoscaler \
--set tolerations[0].key=node_pool \
--set tolerations[0].operator=Equal \
--set tolerations[0].value=tooling \
--set secret.create=false \
perfectscale/psc-autoscaler
If you utilize CI/CD tools with auto-sync, ensure to review the additional configuration steps. /pages/K6pnJVnSY6ckbd69UwMr
Follow the troubleshooting instructions provided, or contact support if you encounter any issues.
Verify Automation Agent Installation
To test and confirm that the Automation Agent was installed successfully, run the following command:
helm test psc-autoscaler -n perfectscale --logs
Disabling Webhook for Specific Namespaces
Once the autoscaler is installed, all pods in the cluster go through the admission webhook. You can exclude specific namespaces from this process by disabling the webhook for them. For example:
helm upgrade --install -n perfectscale psc-autoscaler perfectscale/psc-autoscaler \
--set secret.create=false \
--set 'settings.excludedNamespaces={kube-system,ns_to_exclude1,ns_to_exclude2}'
Step 2: Create Custom Resource (CR)
After the installation of the automation agent, it is important to create a cluster automation configuration, as this step is mandatory:
apiVersion: perfectscale.io/v1
kind: ClusterAutomationConfig
metadata:
name: cluster-automation-config
spec:
automation:
operational:
stopAllAutomation: false # Global kill switch for automation (default: false)
cleanupAllAutomation: false # Stops automation and reverts all changes, restoring original resource specifications and settings
Automation is only enabled in configurations where automationMode for the workload type is set to "Enabled". This can be configured in the ClusterAutomationConfig, NamespaceAutomationConfig, or WorkloadAutomationConfig.
Example:
apiVersion: perfectscale.io/v1
kind: ClusterAutomationConfig
metadata:
name: cluster-automation-config
spec:
automation:
operational:
stopAllAutomation: false # Global kill switch for automation (default: false)
cleanupAllAutomation: false # Stops automation and reverts all changes, restoring original resource specifications and settings
workloadTypes:
Deployment:
operational:
automationMode: "Enabled" # Enables automation for the workloads with a specific type in the cluster/namespace
This configuration enables automation for all workloads in a cluster with type: Deployment. Learn more about enabling automation for particular workload types in a cluster, specific namespace, or specific workloads.
Only one cluster-level automation configuration can be applied per cluster. To prevent errors, PerfectScale recommends using consistent naming for cluster-level configurations within the same cluster. This ensures that a new configuration will override the previous one without causing any conflicts.
To help you quickly start using PerfectScale Automation and streamline the optimization process, we provide pre-built Custom Resources (CRs) focused on desired optimization goals. You can easily implement the following CRs and customize them if needed.
PerfectScale recommends you start with the cluster cost-saving automation configuration.
:point_right: Explore more configuration examples at our GitHub.
⚡ Recommended adoption path
To minimize waste and achieve optimal savings, we recommend starting with a simple, cluster-level automation focused on cost reduction.
Create a YAML file with the recommended cluster-level automation configuration using the following template:
apiVersion: perfectscale.io/v1
kind: ClusterAutomationConfig
metadata:
name: cluster-automation-config
spec:
automation:
operational:
stopAllAutomation: false # Global kill switch for automation (default: false)
cleanupAllAutomation: false # Stops automation and reverts all changes, restoring original resource specifications and settings
workloadTypes:
Deployment:
operational:
automationMode: "Enabled" # Enables automation for the workloads with a specific type in the cluster/namespace
restrictions:
DaemonSet:
operational:
automationMode: "Enabled" # Enables automation for the workloads with a specific type in the cluster/namespace
restrictions:
Apply the created configuration by running the following command (where cluster-automation-config.yaml - the name of your file created in the previous step):
kubectl apply -f cluster-automation-config.yaml
Combining cost-savings and performance improvement configuration
The following example of a Cluster-level automation configuration allows you to automate the optimization of the entire cluster, enhancing its performance while maintaining optimal costs.
Create a YAML file with the cluster-level automation configuration using the following template:
apiVersion: perfectscale.io/v1
kind: ClusterAutomationConfig
metadata:
name: cluster-automation-config
spec:
automation:
operational:
stopAllAutomation: false # Global kill switch for automation (default: false)
cleanupAllAutomation: false # Stops automation and reverts all changes, restoring original resource specifications and settings
workloadTypes:
Deployment:
operational:
automationMode: "Enabled" # Enables automation for the workloads with a specific type in the cluster/namespace
restrictions:
workloadMinWasteUSDPerMonth: 5 # Activates automation only if monthly waste exceeds a set threshold (for example, $5)
cpuManagement:
request:
increaseEnabled: true # Allows PerfectScale Automation to increase CPU requests if the performance risks are observed
decreaseEnabled: true # Allows PerfectScale Automation to decrease CPU requests to minimize waste
limit:
keepLimit: true # Automation will maintain a non-zero CPU limit and will not set the value to zero
memoryManagement:
request:
increaseEnabled: true # Allows PerfectScale Automation to increase Memory requests if the performance risks are observed
decreaseEnabled: true # Allows PerfectScale Automation to decrease Memory requests to minimize waste
limit:
increaseEnabled: true # Allows PerfectScale Automation to increase Memory limit if the performance risks are observed
decreaseEnabled: true # Allows PerfectScale Automation to decrease Memory limit
DaemonSet:
operational:
automationMode: "Enabled" # Enables automation for the workloads with a specific type in the cluster/namespace
restrictions:
workloadMinWasteUSDPerMonth: 5 # Activates automation only if monthly waste exceeds a set threshold (for example, $5)
cpuManagement:
request:
increaseEnabled: true # Allows PerfectScale Automation to increase CPU requests if the performance risks are observed
decreaseEnabled: true # Allows PerfectScale Automation to decrease CPU requests to minimize waste
limit:
keepLimit: true # Automation will maintain a non-zero CPU limit and will not set the value to zero
memoryManagement:
request:
increaseEnabled: true # Allows PerfectScale Automation to increase Memory requests if the performance risks are observed
decreaseEnabled: true # Allows PerfectScale Automation to decrease Memory requests to minimize waste
limit:
increaseEnabled: true # Allows PerfectScale Automation to increase Memory limit if the performance risks are observed
decreaseEnabled: true # Allows PerfectScale Automation to decrease Memory limit
Apply the created configuration by running the following command (where cluster-automation-config.yaml - the name of your file created in the previous step):
kubectl apply -f cluster-automation-config.yaml
Let's explore additional examples of automation configurations that offer more granular and flexible control over Automation:
- How to include a cluster, namespace, or workload in the Automation configuration
- How to exclude a namespace or workload from the Automation configuration
Automation configuration defaults
Some automation configuration settings have predefined defaults. If you don’t specify a value, PerfectScale will automatically apply the default for that field. Below is the list of configurations and their default values.
| Config | Default |
|---|---|
| stopAllAutomation | false |
| cleanupAllAutomation | false |
| automationMode | Enabled |
| workloadLabelSelectors | nil |
| wasteMaxAutomationFrequency | Deployment: 30m Rollout: 30m CronJob: 30m Job: 30m DaemonSet: 4h StatefulSet: 24h |
| fixResiliencyMaxAutomationFrequency | 30m |
| maintenanceWindowIgnoredForResiliency | false |
| maintenanceWindowIgnoredMinResiliencyLevel | "" |
| maintenanceWindow | |
| workloadMinWasteUSDPerMonth | 5 |
| cpuManagement | request: IncreaseEnabled: false DecreaseEnabled: true MinimumCores: -1 // no limit MaximumCores: -1 // no limit limit: KeepLimit: true |
| memoryManagement | request: IncreaseEnabled: false DecreaseEnabled: true MinimumGiB: -1 // no limit MaximumGib: -1 // no limit limit: IncreaseEnabled: false DecreaseEnabled: false MinimumGiB: -1 // no limit MaximumGib: -1 // no limit maxMemoryIncreaseIterations: Daily: 3 Weekly: 6 |