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Posted to issues@yunikorn.apache.org by "Wilfred Spiegelenburg (Jira)" <ji...@apache.org> on 2022/08/15 10:55:00 UTC
[jira] [Updated] (YUNIKORN-830) Node sorting support "preferredDuringScheduling" node affinity
[ https://issues.apache.org/jira/browse/YUNIKORN-830?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wilfred Spiegelenburg updated YUNIKORN-830:
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Target Version: (was: 1.1.0)
Removing target for now as part of the 1.1 prep, it has been a year and no update.
If you want this to be re-scheduled for 1.2 please set the target version again.
> Node sorting support "preferredDuringScheduling" node affinity
> --------------------------------------------------------------
>
> Key: YUNIKORN-830
> URL: https://issues.apache.org/jira/browse/YUNIKORN-830
> Project: Apache YuniKorn
> Issue Type: New Feature
> Components: core - scheduler
> Reporter: Chenya Zhang
> Assignee: Chenya Zhang
> Priority: Major
>
> YuniKorn scheduler is able to allocate pods based on the node affinity policy "requiredDuringScheduling", but not "preferredDuringScheduling" yet.
> YuniKorn currently does a full node sorting every time. After [https://github.com/apache/incubator-yunikorn-core/pull/307], we will do sorting incrementally with an ordered B-tree. It can make scheduling different pods to preferred nodes a bit complicated.
> Discussed with [~cheersyang] [~yuchaoran2011] for a potential solution:
> Maintain a preferred node list for certain labelled pods (configurable, e.g. spark driver pod in this case).
> * Parse node label’s from the shim and send that to the core over SI (via node attributes).
> * Parse the pod node-affinity preference in the shim and send that to the core.
> * Implement something in GetSchedulableNodeIterator().. today we directly retrieve nodes from btree with asce order; we still loop the nodes once, but keep 2 list, one for preferred nodes; when we iterate nodes in the scheduling cycle, we iterate the preferred list first.
> Adding [~wilfreds] [~kmarton] [~ccondit-target] for more discussion too.
> Slack discussion: [https://yunikornworkspace.slack.com/archives/CL9CRJ1KM/p1630088069009500]
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