Adding proposal for workload affinity and anti-affinity support

[mszacillo]

What type of PR is this?
/kind feature

What this PR does / why we need it:
This PR submits a proposal for adding workload affinity and workload anti-affinity as rules that the user can define on the PropagationPolicy. These rules would allow Karmada to support more complex scheduling challenges such as:

• Spreading duplicate FlinkDeployment pipelines across clusters to guarantee HA
• Scheduling many smaller training jobs to be co-located in same cluster

[gemini-code-assist]

Summary of Changes

Hello @mszacillo, 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 to enhance Karmada's scheduling capabilities by adding support for workload affinity and anti-affinity rules to the PropagationPolicy API. This feature aims to address advanced scheduling requirements, such as ensuring high availability for critical data pipelines by spreading duplicate workloads across different clusters (anti-affinity) and co-locating related smaller training jobs or interdependent services within the same cluster to minimize latency (affinity). The proposal outlines the necessary API extensions and architectural changes to the Karmada scheduler to implement these new rules.

Highlights

• API Extension for Workload Affinity/Anti-Affinity: Proposes new WorkloadAntiAffinity and WorkloadAffinity fields within the PropagationPolicy's Placement struct, along with WorkloadAffinityTerm and WeightedWorkloadAffinityTerm definitions, mirroring Kubernetes pod affinity concepts.
• Enhanced High Availability: Introduces anti-affinity rules to enable users to spread duplicate data processing pipelines across different clusters, ensuring continuous operation even if one cluster experiences issues.
• Optimized Workload Co-location: Provides affinity rules to allow users to schedule related workloads, such as distributed training jobs or interdependent services (e.g., Flink with Kafka, Ray with Redis), to the same cluster for improved performance and reduced network latency.
• Scheduler and Cache Modifications: Details required changes to ClusterInfo and ClusterSnapshot to track affinity metadata, and outlines modifications to the Karmada scheduler's internal cache and the addition of a new Workload Affinity Filter Plugin to enforce these rules.

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[gemini-code-assist]

Code Review

This pull request introduces a detailed proposal for adding workload affinity and anti-affinity support to Karmada. The proposal is well-structured, covering user stories, API changes, and a high-level implementation plan. The design thoughtfully borrows concepts from Kubernetes pod affinity/anti-affinity, which promotes consistency. My review includes a few minor suggestions to fix typos and a copy-paste error in comments within the proposal document to improve clarity.

[codecov-commenter]

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Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 45.84%. Comparing base (00d5f45) to head (ade74d6).
⚠️ Report is 122 commits behind head on master.
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Additional details and impacted files

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Flag	Coverage Δ
unittests	45.84% <ø> (+0.47%)	⬆️

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[RainbowMango]

Thank you @mszacillo . We can try to start it in release-1.16.
/milstone v1.16

[mszacillo]

Hi @kevin-wangzefeng, we went over this proposal (specifically going over the use-cases and the API design) during the community meeting today. I think the next step here is agreeing on the API changes, and once that is solidified, starting to determine an implementation strategy. Was hoping you could take a look, given your contributions to this feature in Kubernetes. Thank you!!

[RainbowMango]

/assign

[RainbowMango]

Looks pretty good to me.

[karmada-bot]

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[mszacillo]

Thanks for the review @RainbowMango! I wasn't sure whether to be more thorough with the implementation details - perhaps we can discuss this more in the next community meeting. I can start to draft some iteration tasks in the meantime.

[RainbowMango]

I was focusing on the API changes part during last review. I will leave my comments here about the code changes part. Also, I hope to explore the way how Kubernetes implements the affinity/anti-affinity feature.