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Posted to yarn-issues@hadoop.apache.org by "He Tianyi (JIRA)" <ji...@apache.org> on 2017/02/04 01:54:51 UTC
[jira] [Commented] (YARN-6101) Delay scheduling for node resource
balance
[ https://issues.apache.org/jira/browse/YARN-6101?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15852480#comment-15852480 ]
He Tianyi commented on YARN-6101:
---------------------------------
Thanks for reply, [~tangzhankun].
It's a typo, memory should be 1 GB left.
I cannot share raw SLS configuration since it is generated directly from a production cluster (with 2500 nodes), by tracking all applications submitted on that day, including Spark, MapReduce and other frameworks.
> Delay scheduling for node resource balance
> ------------------------------------------
>
> Key: YARN-6101
> URL: https://issues.apache.org/jira/browse/YARN-6101
> Project: Hadoop YARN
> Issue Type: Improvement
> Components: fairscheduler
> Reporter: He Tianyi
> Priority: Minor
> Attachments: YARN-6101.preliminary.0000.patch
>
>
> We observed that, in today's cluster, usage of Spark has dramatically increased.
> This introduced a new issue that CPU/MEM utilization for single node may become imbalanced due to Spark is generally more memory intensive. For example, after a node with capability (48 cores, 192 GB memory) cannot satisfy a (1 core, 2 GB memory) request if current used resource is (20 cores, 191 GB memory), with plenty of total available resource across the whole cluster.
> A thought for avoiding the situation is to introduce some strategy during scheduling.
> This JIRA proposes a delay-scheduling-alike approach to achieve better balance between different type of resources on each node.
> The basic idea is consider dominant resource for each node, and when a scheduling opportunity on a particular node is offered to a resource request, better make sure the allocation is changing dominant resource of the node, or, in worst case, allocate at once when number of offered scheduling opportunities exceeds a certain number.
> With YARN SLS and a simulation file with hybrid workload (MR+Spark), the approach improved cluster resource usage by nearly 5%. And after deployed to production, we observed a 8% improvement.
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