github.com/gocrane/crane@v0.11.0/docs/proposals/20220706-universal-resource-optimization.md (about)

     1  # Universal Resource Optimization 
     2  - Universal Resource Optimization provide a consistence progress to optimize variable kinds of resources in kubernetes. The progress should be Pluggable and support Multi-Cloud.
     3  
     4  ## Table of Contents
     5  
     6  <!-- TOC -->
     7  
     8  
     9  <!-- /TOC -->
    10  ## Motivation
    11  Currently, we use `Analytics` and `Recommendation` to provide a recommendation service for workloads in cluster. Kubernetes' users use the recommendation to optimize the resource configuration and reduce their cost.
    12  But the recommendations have some limitations now:
    13  
    14  1. Multiple Analytics can select some same resources, it's confused and unnecessary to have two recommendation for the same resource. 
    15  2. We need to support more kinds of resources, for example, scan for idle load balancers.
    16  3. We need to make the progress Pluggable to support different user in difference clouds.
    17  
    18  ### Goals
    19  
    20  - Global analytics rules
    21  - Easy to know the recommendation for my resource
    22  - Consistence progress for all resource recommendation
    23  - Plugin mechanism to support Multi-Cloud
    24  
    25  ### Non-Goals
    26  
    27  - Cloud Resources that not included in kubernetes
    28  
    29  ## Proposal
    30  
    31  Recommendation Definition
    32  
    33  Recommendation Framework
    34  
    35  ### User Stories
    36  
    37  #### Story 1
    38    As a Serverless customer, I want to know the suitable requests and limits for my deployments, the result should be fit the existing pod model(e.g. 2c4g, 1c1g) in my cloud production.
    39  #### Story 2
    40    As an Aliyun ACK customer, I want to know whether there is a waste of LoadBalances in my cluster and delete them if exists.  
    41  #### Story 3
    42    As a container platform user, I want to integrate optimize recommendation to my platform and optimize my cluster within my CICD pipeline.
    43  
    44