Nowadays, multi-cluster workload deployment and management have become increasingly common. Users usually use HPA for scaling workloads to meet changing demands. However, the current autoscaling is limited to a single cluster even when using multi-clusters. If we can break through the cluster boundaries, there will be some awesome scenarios, including scaling across clusters to extend resources infinitely, scaling up workloads in the local IDC first before the public cloud to save costs, and so on.

To bring the benefits of autoscaling across clusters to users, we designed and implemented two types of multi-cluster HPA: centralized and distributed. They are different and have their own appropriate scenarios.

In this session, Wei and XingYan will go over:
1. The challenges, benefits, and scenarios of autoscaling across clusters.
2. How we implement them in Karmada to solve the challenges.
3. How to select the appropriate type for different scenarios and example demonstrations.