[proposal]Two better resource scheduling and allocation plugins #842
Description
What would you like to be added?
What is your proposal:
The NodeResourcesFit plug-in of native k8s can only adopt a type of strategy for all resources, such as MostRequestedPriority and LeastRequestedPriority. However, in industrial practice, this design does not apply to some scenarios. For example: In AI scenarios, businesses that apply for GPUs prefer to occupy the entire GPU machine first to prevent GPU fragmentation; businesses that apply for CPU & MEM are prioritized and dispersed to non-GPU machines to prevent excessive consumption of CPU & MEM on GPU machines, resulting in real tasks of applying for GPUs. Pending due to insufficient non-GPU resources
. It is therefore hoped that both strategies can be extended to address this business need.
Why is this needed:
There are related descriptions above
Is there a suggested solution, if so, please add it:
plugin-one
config：
resources:
nvidia.com/gpu:
type: MostAllocated
weight: 2
cpu:
type: LeastAllocated
weight: 1
memory:
type: LeastAllocated
weight: 1
config description：
node score:
finalScoreNode = [(weight1 * resource1) + (weight2 * resource2) + … + (weightN* resourceN)] /(weight1+weight2+ … +weightN)
plugin-two
config：
resources:
- nvidia.com/gpu
config description：
node score:
finalScoreNode = (allocatablesResourcesNum - requestsResourcesNum) * framework.MaxNodeScore / allocatablesResourcesNum
Why is this needed?
It’s introduced above