Support scaling behaviors and remove configuration duplication

[Jeffwan]

Pull Request Description

• Make ScalingContext the single source of truth for PA configuration
• Remove configurable stable-window and also introduce cool-down window sematics
• Remove configuration duplication

Related Issues

Resolves: part of #1422

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[gemini-code-assist]

Summary of Changes

Hello @Jeffwan, 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 significantly refactors the autoscaling logic by centralizing all PodAutoscaler configuration into a single ScalingContext. This change eliminates configuration duplication, simplifies the codebase, and introduces more robust scaling behaviors, including fluctuation tolerances and cooldown windows for both KPA/APA and HPA strategies. The primary goal is to provide a more consistent and controllable autoscaling experience.

Highlights

• Configuration Centralization: The ScalingContext has been established as the single source of truth for all PodAutoscaler (PA) configuration, consolidating various scaling parameters into one interface and removing redundant configuration extraction logic.
• Removal of Duplicated Configuration: The dedicated config package and its ConfigExtractor have been removed, streamlining the configuration process and eliminating duplication by moving configuration parsing directly into the ScalingContext.
• Enhanced Scaling Behaviors: New scaling parameters such as UpFluctuationTolerance, DownFluctuationTolerance, ScaleUpCooldownWindow, ScaleDownCooldownWindow, and PanicThreshold have been introduced and integrated into the ScalingContext, allowing for more nuanced and stable scaling decisions.
• Cooldown Window Implementation: A new mechanism for applying cooldown windows to KPA/APA scaling recommendations has been implemented, storing a history of recommendations to prevent rapid, erratic scaling, similar to Kubernetes HPA's stabilization logic.
• HPA Behavior Customization: The HorizontalPodAutoscaler (HPA) behavior can now be customized directly from PodAutoscaler annotations, allowing users to define stabilization windows and scaling rates for HPA-managed autoscaling.

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[gemini-code-assist]

Code Review

This pull request is a significant and valuable refactoring that centralizes the PodAutoscaler configuration into ScalingContext, removes duplicated configuration, and introduces cooldown window semantics. The changes make the system more maintainable and align with the stated goals. However, I've identified a few issues, including a critical bug in the new KPA tolerance logic, a potential memory leak in the recommendation stabilization logic, and some code duplication that goes against the PR's intent. Addressing these points will greatly improve the quality and correctness of this contribution.

[Jeffwan]

/gemini review

[gemini-code-assist]

Code Review

This pull request is a significant and well-executed refactoring that centralizes PodAutoscaler configuration into ScalingContext, effectively removing duplication and simplifying the overall design. The introduction of cooldown window semantics for KPA/APA and configurable behavior for HPA are excellent feature enhancements that will improve stability and control. My review focuses on a few opportunities to further improve maintainability by reducing code duplication and enhancing observability for configuration fallbacks.