Feature Overview:

Enable support for AWS Capacity Blocks in HCP (NodePool API)  to allow users to guarantee access to reserved EC2 instances, particularly for critical workloads requiring specialized hardware like GPUs.

Goals:

• Users can create and manage Capacity Blocks through HCP (NodePool API).
• Users can associate Machine Pools with Capacity Blocks to guarantee resource availability.

Requirements:

• NodePool API Extension: Implement necessary API changes to expose Capacity Blocks in NodePool.
  • Ability to create, view, and delete Capacity Blocks.
  • Ability to associate Machine Pools with Capacity Blocks.
  • Ability to specify instance type, quantity, and duration for Capacity Blocks.

• User Documentation: Provide documentation on how to use Capacity Blocks within HCP.

Deployment Considerations:

• Self-managed, managed, or both: both 
• Classic (standalone cluster): N/A
• Hosted control planes: Yes (HCP)
• Multi node, Compact (three node), or Single node (SNO), or all: N/A
• Connected / Restricted Network: Both
• Architectures: x86_64 (initially)
• Operator compatibility: N/A
• Backport needed: N/A
• UI need: OpenShift Console and/or OCM
• Other:  Consider integration with cost management tools.

Use Cases:

• A data science team needs to ensure access to a specific number of GPU instances for training their machine learning models. They can use Capacity Blocks to reserve these instances, guaranteeing resource availability for their critical workload.
• A company running a high-performance computing cluster wants to guarantee access to specialized instances for their simulations. Capacity Blocks can provide this assurance, preventing resource contention and ensuring consistent performance.

Background:

The increasing demand for specialized hardware, especially GPUs, has made it challenging for users to guarantee access to these resources. AWS Capacity Blocks provide a solution to this problem by allowing users to reserve instances for a specific duration. This feature aims to integrate Capacity Block support into HCP to provide users with this capability.

Customer Considerations:

This feature will primarily benefit customers running critical workloads that require guaranteed access to specific instance types, such as those using GPUs for AI/ML or high-performance computing.

Documentation Considerations:

Documentation should be updated to guide users through creating, managing, and utilizing Capacity Blocks within HCP. This should include information on best practices and limitations.

Interoperability Considerations:

This feature will primarily impact HCP and its integration with AWS. Potential interoperability concerns with other projects like ROSA/OSD/ARO (for similar concepts if they exist) should be investigated.