feat: add kep md #845

LY-today · 2024-12-24T03:57:09Z

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

Signed-off-by: LY-today <[email protected]>

k8s-ci-robot · 2024-12-24T03:57:12Z

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k8s-ci-robot · 2024-12-24T03:57:14Z

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k8s-ci-robot · 2024-12-24T03:57:18Z

Hi @LY-today. Thanks for your PR.

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LY-today · 2024-12-24T06:37:45Z

@googs1025 KEP

Signed-off-by: LY-today <[email protected]>

LY-today · 2024-12-25T07:25:08Z

@googs1025 KEP

@googs1025 Can you help advance this MR?

googs1025 · 2024-12-25T09:34:39Z

@googs1025 KEP

@googs1025 Can you help advance this MR?

Thanks for the invite, I'll handle this on weekend :)

LY-today · 2024-12-25T09:48:12Z

@googs1025 KEP

@googs1025 Can you help advance this MR?

Thanks for the invite, I'll handle this on weekend :)

thank you for your time

googs1025 · 2024-12-29T08:38:11Z

kep/625-node-resource-fit-plus-scoring/README.md

@@ -0,0 +1,114 @@
+# Node Resource Fit plus Scheduling


This seems very similar to an old plugin. Can you help to tell the difference or integrate it?
FYI: https://github.com/kubernetes-sigs/scheduler-plugins/tree/master/kep/48-node-resources-allocatable-scoring

@googs1025 Older version policies can only use one policy for different resources. Not suitable for complex resource scenarios, such as AI

@googs1025 Under the AI cluster. It is hoped that GPU tasks will be scheduled on one GPU machine as much as possible, and CPU tasks will be scattered on CPU machines. However, the old version of the policy does not support using different policies for the two resources.

As I mentioned, it seems to be very similar to the previous nodeResourcesAllocatable, and I don't think it needs to be extended with a new plugin. If it is possible, can it be integrated into the original plugin? 🤔

@googs1025 What you mean is that you agree with the design of the NodeResourcesFitPlus strategy, but you want to implement it by modifying the original nodeResourcesAllocatable strategy?

@googs1025 What you mean is that you agree with the design of the NodeResourcesFitPlus strategy, but you want to implement it by modifying the original nodeResourcesAllocatable strategy?

@googs1025 Do I understand correctly?

googs1025 · 2024-12-29T08:40:11Z

kep/625-node-resource-fit-plus-scoring/images/img1.png

It is not recommended to use screenshots of tables and pictures.

I'm going to make adjustments

googs1025 · 2024-12-29T08:45:17Z

kep/625-node-resource-fit-plus-scoring/README.md

+
+## Summary
+
+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


In the scenario of using gpu nodes, which are scarce resources, we should directly filter out the gpu nodes. Shouldn't this reduce the score? In addition, IIUC, gpu nodes (or other devices) are labeled (based on gpu-operator or nfd), and are generally filtered in this way.

Affinity strategies or nodeSelector require labeling nodes in advance, which is costly for cluster maintainers. The advantage of the strategy is to reduce this maintenance operation

I think quite the opposite, we should have provided feature tags for device-specific nodes (eg: nvidia.com/gpu.xxx). 🤔

Understand, labels can indeed be printed to distinguish machine types. Of course, it can also be done using the Affinity strategy. But what I want to say is that the above process has costs at the industrial practice level. 100 heterogeneous resources require the maintenance cost of 100 sets of labels.

@googs1025 If you think that maintenance cost is not something that k8s needs to consider, then indeed the second expansion strategy does not need to be incorporated.

@googs1025 Does the ScarceResourceAvoidance strategy have a clear conclusion? Accept or not?

This is not for me to decide and can be left to other maintainers to suggest.

@googs1025 Thank you for your feedback. Can you help me let other students review it?

LY-today · 2024-12-31T03:21:57Z

@googs1025 Hello, do you have any clear plans for these two plugins?

LY-today · 2025-01-02T07:58:14Z

@swatisehgal @zwpaper Please check

feat: add kep

fe07ce9

Signed-off-by: LY-today <[email protected]>

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LY-today mentioned this pull request Dec 24, 2024

feat: add NodeResourcesFitPlus and ScarceResourceAvoidance plugin #843

Open

fix: update kep md

956d7f5

Signed-off-by: LY-today <[email protected]>

LY-today changed the title ~~feat: add kep~~ feat: add kep md Dec 24, 2024

googs1025 reviewed Dec 29, 2024

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feat: add kep md #845

feat: add kep md #845

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LY-today commented Jan 2, 2025


		## Summary

		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

feat: add kep md #845

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feat: add kep md #845

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LY-today commented Dec 24, 2024

What would you like to be added?

plugin-one

plugin-two

Why is this needed?

k8s-ci-robot commented Dec 24, 2024

k8s-ci-robot commented Dec 24, 2024

k8s-ci-robot commented Dec 24, 2024

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