Scaling a Workload

Scaling policy priority: If you do not manually adjust the number of pods, auto scaling policies will take effect for resource scheduling. If manual scaling is triggered, auto scaling policies will be temporarily invalid.

Auto Scaling - HPA

HPA policies can be used for auto scaling. You can view all policies or perform more operations in Auto Scaling.

Auto Scaling - AOM

You can define auto scaling policies as required, which can intelligently adjust resources in response to service changes and data traffic spikes.

Auto scaling can be backed by Application Operations Management (AOM), but not for clusters of v1.17 and later.

Currently, CCE supports the following types of auto scaling policies:

Metric-based policy: After a workload is created, pods will be automatically scaled when the workload's CPU or memory usage exceeds or falls below a preset limit.

Scheduled policy: scaling at a specified time. Scheduled auto scaling is applicable flash sales, premier shopping events, and other regular events that bring a high burst of traffic load.

Periodic policy: scaling at a specified time on a daily, weekly, or monthly basis. Periodic scheduling is applicable to scenarios where traffic changes periodically.

Manual Scaling

  1. Log in to the CCE console. In the navigation pane, choose Workloads > Deployments or StatefulSets. In the same row as the target workload, choose More > Scaling.
  2. In the Manual Scaling area, click and change the number of pods to, for example, 3. Then, click Save. The scaling takes effect immediately.
  3. On the Pods tab page, check that a new pod is being created. When the pod status becomes Running, pod scaling is complete.