Support for recommending resource specifications for workloads

[caohe]

Why is this needed?

Kubernetes is widely adopted for its ability to manage containerized workloads efficiently. However, determining the appropriate resource specifications (CPU and memory) for workloads remains a challenge. Often, users over-provision resources to ensure performance stability, leading to wasted resources and increased costs. On the other hand, under-provisioning can result in performance degradation and service disruptions.
By introducing a resource recommendation feature, we can address these challenges and provide the following benefits:

• Resource Efficiency: Users will be able to allocate resources more accurately, reducing waste and optimizing cost management.
• Performance Optimization: Tailored recommendations will ensure that workloads have the resources they need to run optimally, minimizing both over-provisioning and performance bottlenecks.
• Ease of Use: The automated recommendation process will simplify resource management for both experienced and novice Kubernetes users.

What would you like to be added?

This issue proposes the addition of a new feature that enhances Kubernetes resource utilization by providing the ability to recommend resource specifications for workloads. This feature would analyze historical usage patterns and real-time performance metrics of workloads to intelligently suggest optimal resource requests for CPU and memory.

[ZhangsongLee]

/assign