Horizontal Pod Autoscaler (HPA)

Constraints

Each pod runs in an environment where system components are installed. These system components occupy some resources, which may cause the resource usages of the pod to be lower than the expected limits. As a result, the triggering conditions of the HPA policy you configure may be affected. To avoid this, refer to Reserved System Overhead.

Creating and Managing an HPA Policy on the Console

1. Log in to the CCI 2.0 console.
2. In the navigation pane, choose Workloads. On the Deployments tab, locate the target Deployment and click its name.
3. On the Deployment details page, click the Auto Scaling tab.
4. Click Create HPA Policy and supplement related information as prompted. For details about the parameters, see Table 1.

   Table 1 Parameters for creating an HPA policy

   Parameter	Description
   Name	Enter an HPA policy name.
   Namespace	Select a namespace. If you need to create a namespace, click Create Namespace.
   Workload	CCI automatically matches the associated workload.
   Pod Range	Enter the maximum and minimum numbers of pods that can be scaled by HPA.
   Scaling Behavior	Default Workloads will be scaled using the Kubernetes default behavior. Custom Workloads will be scaled using custom policies such as the stabilization window, steps, and priorities. Unspecified parameters use the values recommended by Kubernetes.
   System Policies	Select the required metrics and set the desired values.

   

5. Optional: Click Update HPA Policy or Delete HPA Policy to update or delete a created HPA policy.

Creating an HPA Policy Using a YAML File

• If spec.metrics.resource.target.type is set to Utilization, you need to specify the resource requests value when creating a workload.
• When spec.metrics.resource.target.type is set to Utilization, the resource usage is calculated as follows: Resource usage = Used resource/Available pod flavor. You can determine the actual flavor of a pod by referring to Pod Flavor.

A properly configured auto scaling policy includes the metric, threshold, and step. It eliminates the need to manually adjust resources in response to service changes and traffic bursts, thus helping you reduce workforce and resource consumption.

CCI supports auto scaling based on metrics. The workloads are scaled out or in based on the CPU or memory usage. You can specify a CPU or memory usage threshold. If the usage is higher or lower than the threshold, pods are automatically added or deleted.

• Configure a metric-based auto scaling policy.
  1. Log in to the CCI 2.0 console.
  2. In the navigation pane, choose Workloads. On the Deployments tab, locate the target Deployment and click its name.
  3. On the Auto Scaling tab, click Create from YAML to configure a policy.

     The following describes the auto scaling policy file format:

     • Resource description in the hpa.yaml file

       kind: HorizontalPodAutoscaler
       apiVersion: cci/v2
       metadata:
         name: nginx              # HPA name
         namespace: test          # HPA namespace
       spec:
         scaleTargetRef:         # Reference the resource to be automatically scaled.
           kind: Deployment      # Type of the target resource, for example, Deployment
           name: nginx           # Name of the target resource
           apiVersion: cci/v2    # Version of the target resource
         minReplicas: 1          # Minimum number of replicas for HPA scaling
         maxReplicas: 5          # Maximum number of replicas for HPA scaling
         metrics:
           - type: Resource                 # Resource metrics are used.
             resource:
               name: memory                 # Resource name, for example, cpu or memory
               target:
                 type: Utilization          # Metric type. The value can be Utilization (percentage) or AverageValue (absolute value).
                 averageUtilization: 50     # Resource usage. For example, when the CPU usage reaches 50%, scale-out is triggered.
         behavior:
           scaleUp:
             stabilizationWindowSeconds: 30  # Scale-out stabilization duration, in seconds
             policies:
             - type: Pods           # Number of pods to be scaled
               value: 1
               periodSeconds: 30  # The check is performed once every 30 seconds.
           scaleDown:
             stabilizationWindowSeconds: 30  # Scale-in stabilization duration, in seconds
             policies:
             - type: Percent      # The resource is scaled in or out based on the percentage of existing pods.
               value: 50
               periodSeconds: 30  # The check is performed once every 30 seconds.

     • Resource description in the hpa.json file

       {
       	"kind": "HorizontalPodAutoscaler",
       	"apiVersion": "cci/v2",
       	"metadata": {
       		"name": "nginx",             # HPA name
       		"namespace": "test"          # HPA namespace
       	},
       	"spec": {
       		"scaleTargetRef": {         # Reference the resource to be automatically scaled.
       			"kind": "Deployment",        # Type of the target resource, for example, Deployment
       			"name": "nginx",             # Name of the target resource
       			"apiVersion": "cci/v2"       # Version of the target resource
       		},
       		"minReplicas": 1,             # Minimum number of replicas for HPA scaling
       		"maxReplicas": 5,             # Maximum number of replicas for HPA scaling
       		"metrics": [
       			{
       				"type": "Resource",             # Resource metrics are used.
       				"resource": {
       					"name": "memory",       # Resource name, for example, cpu or memory
       					"target": {
       						"type": "Utilization",           # Metric type. The value can be Utilization (percentage) or AverageValue (absolute value).
       						"averageUtilization": 50         # Resource usage. For example, when the CPU usage reaches 50%, scale-out is triggered.
       					}
       				}
       			}
       		],
       		"behavior": {
       			"scaleUp": {
       				"stabilizationWindowSeconds": 30,
       				"policies": [
       					{
       						"type": "Pods",
       						"value": 1,
       						"periodSeconds": 30
       					}
       				]
       			},
       			"scaleDown": {
       				"stabilizationWindowSeconds": 30,
       				"policies": [
       					{
       						"type": "Percent",
       						"value": 50,
       						"periodSeconds": 30
       					}
       				]
       			}
       		}
       	}
       }

  4. Click OK.
     You can view the policy on the Auto Scaling tab.

     Figure 1 Auto scaling policy
     

     When the trigger condition is met, the auto scaling policy will be executed.