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https://issues.apache.org/jira/browse/YARN-3806?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wei Shao updated YARN-3806:
---------------------------
    Description: 
Currently, a typical YARN cluster runs many different kinds of applications: 
production applications, ad hoc user applications, long running services and so 
on. Different YARN scheduling policies may be suitable for different 
applications. For example, capacity scheduling can manage production 
applications well since application can get guaranteed resource share, fair 
scheduling can manage ad hoc user applications well since it can enforce 
fairness among users. However, current YARN scheduling framework doesn’t have a 
mechanism for multiple scheduling policies work hierarchically in one cluster.

YARN-3306 talked about many issues of today’s YARN scheduling framework, and 
proposed a per-queue policy driven framework. In detail, it supported different 
scheduling policies for leaf queues. However, support of different scheduling 
policies for upper level queues is not seriously considered yet. 

A generic scheduling framework is proposed here to address these limitations. 
It supports different policies for any queue consistently. The proposal tries 
to solve many other issues in current YARN scheduling framework as well.

  was:
Currently, A typical YARN cluster runs many different kinds of applications: 
production applications, ad hoc user applications, long running services and so 
on. Different YARN scheduling policies may be suitable for different 
applications. For example, capacity scheduling can manage production 
applications well since application can get guaranteed resource share, fair 
scheduling can manage ad hoc user applications well since it can enforce 
fairness among users. However, current YARN scheduling framework doesn’t have a 
mechanism for multiple scheduling policies work hierarchically in one cluster.

YARN-3306 talked about many issues of today’s YARN scheduling framework, and 
proposed a per-queue policy driven framework. In detail, it supported different 
scheduling policies for leaf queues. However, support of different scheduling 
policies for upper level queues is not seriously considered yet. 

A generic scheduling framework is proposed here to address these limitations. 
It supports different policies for any queue consistently. The proposal tries 
to solve many other issues in current YARN scheduling framework as well.


> Proposal of Generic Scheduling Framework for YARN
> -------------------------------------------------
>
>                 Key: YARN-3806
>                 URL: https://issues.apache.org/jira/browse/YARN-3806
>             Project: Hadoop YARN
>          Issue Type: Improvement
>          Components: scheduler
>            Reporter: Wei Shao
>         Attachments: ProposalOfGenericSchedulingFrameworkForYARN-V1.0.pdf, 
> ProposalOfGenericSchedulingFrameworkForYARN-V1.1.pdf
>
>
> Currently, a typical YARN cluster runs many different kinds of applications: 
> production applications, ad hoc user applications, long running services and 
> so on. Different YARN scheduling policies may be suitable for different 
> applications. For example, capacity scheduling can manage production 
> applications well since application can get guaranteed resource share, fair 
> scheduling can manage ad hoc user applications well since it can enforce 
> fairness among users. However, current YARN scheduling framework doesn’t have 
> a mechanism for multiple scheduling policies work hierarchically in one 
> cluster.
> YARN-3306 talked about many issues of today’s YARN scheduling framework, and 
> proposed a per-queue policy driven framework. In detail, it supported 
> different scheduling policies for leaf queues. However, support of different 
> scheduling policies for upper level queues is not seriously considered yet. 
> A generic scheduling framework is proposed here to address these limitations. 
> It supports different policies for any queue consistently. The proposal tries 
> to solve many other issues in current YARN scheduling framework as well.



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