proposal(scheduler): cascading scheduling

[zhzhuang-zju]

What type of PR is this?
/kind documentation

What this PR does / why we need it:
Karmada currently supports declaring a set of candidate clusters through clusterAffinity, or multiple sets of candidate clusters through ClusterAffinities (which combines multiple clusterAffinity terms in a specific order). However, in either approach, each clusterAffinity represents an independent, mutually exclusive cluster set during a single scheduling process—the scheduler ultimately selects only one cluster group defined by one clusterAffinity or its subset.

This model has limitations in hybrid cloud scenarios (such as coexistence of local data centers and public clouds). In practical use, local clusters typically serve as the preferred resource pool, while public cloud clusters act as extensions or backup resources. The two are not completely independent and mutually exclusive relationships, but should be automatically used supplementarily based on priority when local resources are insufficient.

To address this, this proposal introduces cascading cluster affinity scheduling to describe priority relationships between cluster groups. This mechanism will enable Karmada to better support workload scheduling in hybrid cloud environments and improve the deployment practicality of online applications in terms of elasticity.

Which issue(s) this PR fixes:

Parts of #7014

Special notes for your reviewer:

Does this PR introduce a user-facing change?:

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[gemini-code-assist]

Summary of Changes

Hello @zhzhuang-zju, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a comprehensive proposal for "cascading cluster affinity scheduling" in Karmada. The core objective is to enhance workload placement strategies in hybrid cloud environments by allowing users to define prioritized cluster groups. This mechanism ensures that workloads first attempt to utilize preferred, often more cost-effective, clusters and then automatically expand to supplementary clusters when primary resources are insufficient, thereby optimizing resource utilization and supporting elastic scaling. The proposal details API changes, necessary scheduler adjustments, and a test plan to integrate this new functionality.

Highlights

• Introduction of Cascading Scheduling: Introduces a new "cascading cluster affinity scheduling" mechanism to Karmada, allowing for priority-based resource allocation across cluster groups.
• Enhanced Hybrid Cloud Support: Addresses limitations in hybrid cloud scenarios by enabling automatic supplementary use of cluster resources, where primary clusters are preferred and secondary clusters act as extensions or backups.
• Priority-Based Resource Allocation: Workloads will preferentially use primary clusters (e.g., local data centers) and automatically cascade to supplementary clusters (e.g., public clouds) when primary resources are insufficient.
• API Extension Proposals: Presents three distinct API approaches to implement this feature within Karmada's PropagationPolicy and Placement APIs, ensuring flexibility in design.
• Scheduler Logic Adjustment: Requires modifications to the karmada-scheduler to process multiple affinity terms in a cascading manner, adapting its current single-affinity-term logic.
• Backward Compatibility: The proposal emphasizes backward compatibility, introducing entirely new APIs without altering the behavior of existing configurations.

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[zhzhuang-zju]

cc @vie-serendipity @RainbowMango @XiShanYongYe-Chang

[gemini-code-assist]

Code Review

This pull request introduces a design proposal for 'Cascading Cluster Affinity Scheduling' in Karmada. The proposal is well-structured and clearly outlines the motivation, goals, and different API design approaches to enable prioritized cluster selection, which is particularly useful for hybrid cloud environments.

My review focuses on the design choices presented in the proposal. I've provided feedback on the three API design approaches, recommending one for its clarity and maintainability. I also pointed out a need for clarification on how the Duplicated replica scheduling strategy would behave in a failover scenario to ensure the design is comprehensive.

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Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 46.55%. Comparing base (d9abe69) to head (bde7345).
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Additional details and impacted files

@@            Coverage Diff             @@
##           master    #7078      +/-   ##
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- Coverage   46.56%   46.55%   -0.01%     
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  Files         700      700              
  Lines       48103    48103              
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- Hits        22397    22395       -2     
- Misses      24026    24027       +1     
- Partials     1680     1681       +1     

Flag	Coverage Δ
unittests	46.55% <ø> (-0.01%)	⬇️

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[zhzhuang-zju]

Updated the implementation part, see https://github.com/karmada-io/karmada/pull/7078/changes#diff-5ea1bb478c9b042fe6052f25d0bd870c66743aa9b1ca6ebc6f687e5f415fb3a7R189-R205

I've implemented a simple demo locally, and it works well. Here is the link: master...zhzhuang-zju:karmada:poc-cascade. Feel free to give it a try if you're interested.

[RainbowMango]

/assign