突破集群边界，在大规模上自动扩展工作负载 | KC23

Break Through Cluster Boundaries to Autoscale Workloads Across Them on...- Wei Jiang & XingYan Jiang

突破集群边界，在大规模上自动扩展工作负载 | Break Through Cluster Boundaries to Autoscale Workloads Across Them on a Large Scale - Wei Jiang, Huawei Cloud & XingYan Jiang, DaoCloud

现如今，多集群工作负载的部署和管理变得越来越常见。用户通常使用HPA来扩展工作负载以满足不断变化的需求。然而，当前的自动扩展功能仅限于单个集群，即使使用多个集群也是如此。如果我们能突破集群边界，将会有一些令人惊叹的场景，包括跨集群扩展资源以无限延伸、在本地IDC中首先扩展工作负载以节省成本等等。为了将跨集群的自动扩展优势带给用户，我们设计并实现了两种类型的多集群HPA：集中式和分布式。它们各不相同，并且有适用于自己的场景。在本次会议中，Wei和XingYan将介绍：1. 跨集群自动扩展的挑战、优势和应用场景。2. 我们如何在Karmada中实现它们以解决这些挑战。3. 如何选择适合不同场景的类型，并进行示例演示。

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.

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