Positions and directions of multi-cluster system in the AI era

[vie-serendipity]

Hello fellow Karmada contributors and users,

Karmada, as a pioneering multi-cluster system, has tackled numerous issues. I'm a contributor to the community and utilize some of Karmada's capabilities in my work. After working on related tasks for a while, I've come across certain questions, leading to this discussion.

1. Necessity of Multi-Cluster Systems

As an infrastructure-focused developer, I'm figuring out the necessity of multi-cluster systems. Particularly from the perspective of end-users, what's the necessity? I've considered a few aspects, and I welcome any additions and discussions.

• Simplified deployment across many clusters
• Disaster recovery
• Isolation of physical resources in multi-tenant scenarios

During usage, some issues might have arisen, such as the complexity introduced by PropagationPolicy and other CRDs, multi-cluster network connectivity challenges. What's more?

2. Large Models' Impact on Multi-Cluster Management

The emergence of large models might also introduce new challenges to the multi-cluster domain.

• Ultra-large-scale training and inference across multiple clusters (uncertainty if it's actually used)
• Multi-vendor resource provisioning in hybrid clouds

What else?

Looking forward to hearing your thoughts and experiences on these matters.