[jira] [Commented] (YARN-5139) [Umbrella] Move YARN scheduler towards global scheduler

["Wangda Tan (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/YARN-5139?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15298988#comment-15298988 ] 

Wangda Tan commented on YARN-5139:
----------------------------------

One possibly doable approch in my mind is to move the top level loop:
bq. for node in allNodes:
Into application, new logic looks like:
{code}
  Go to parentQueue
    Go to leafQueue
      for application in leafQueue.applications:
        for resource-request in application.resource-requests
          for node in nodes (node candidates sorted according to resource-request)
            try to schedule on node
{code}

Because only application knows which node is best for its pending resource requests, so we can sort and filter node candidates based on application's resource-requests.

> [Umbrella] Move YARN scheduler towards global scheduler
> -------------------------------------------------------
>
>                 Key: YARN-5139
>                 URL: https://issues.apache.org/jira/browse/YARN-5139
>             Project: Hadoop YARN
>          Issue Type: New Feature
>            Reporter: Wangda Tan
>            Assignee: Wangda Tan
>
> Existing YARN scheduler is based on node heartbeat. This can lead to sub-optimal decisions because scheduler can only look at one node at the time when scheduling resources.
> Pseudo code of existing scheduling logic looks like:
> {code}
> for node in allNodes:
>    Go to parentQueue
>       Go to leafQueue
>         for application in leafQueue.applications:
>            for resource-request in application.resource-requests
>               try to schedule on node
> {code}
> Considering future complex resource placement requirements, such as node constraints (give me "a && b || c") or anti-affinity (do not allocate HBase regionsevers and Storm workers on the same host), we may need to consider moving YARN scheduler towards global scheduling.



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