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From Jordà Polo (JIRA) <j...@apache.org>
Subject [jira] Commented: (MAPREDUCE-1380) Adaptive Scheduler
Date Wed, 16 Feb 2011 17:40:25 GMT

    [ https://issues.apache.org/jira/browse/MAPREDUCE-1380?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12995408#comment-12995408

Jordà Polo commented on MAPREDUCE-1380:

I'm sending a new version of the Adaptive Scheduler.

This new version is actually a new implementation with a different architecture roughly described
in the attached PDF document. It supports the same features as the previous version, but at
the same time provides new features and a framework for future improvements.

The new features are mostly focused on making the scheduler more aware of the resources and
allowing a dynamic number of running tasks depending on the jobs and their need for resources
(instead of a fixed number of slots).

It is still a work in progress and requires some additional tuning, but I thought it would
be interesting to publish it as it is now given some of the ideas that have been proposed
for Hadoop MapReduce NextGen (MAPREDUCE-279). The scheduler currently leverages job profiling
information to ensure optimal cluster utilization, but our goal is to get rid of this kind
of profiles and implement a more dynamic approach (e.g. using resource information data introduced
by MAPREDUCE-1218).

I still don't know what's the status of the "NextGen" proposal and its implementation. But
as soon as more details about NextGen are revealed we'll see whether it makes sense and it
is worth/useful to adapt or use some of the ideas in the new Hadoop MapReduce architecture.

> Adaptive Scheduler
> ------------------
>                 Key: MAPREDUCE-1380
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-1380
>             Project: Hadoop Map/Reduce
>          Issue Type: New Feature
>            Reporter: Jordà Polo
>            Priority: Minor
>         Attachments: MAPREDUCE-1380_0.1.patch, MAPREDUCE-1380_1.1.patch
> The Adaptive Scheduler is a pluggable Hadoop scheduler that automatically adjusts the
amount of used resources depending on the performance of jobs and on user-defined high-level
business goals.
> Existing Hadoop schedulers are focused on managing large, static clusters in which nodes
are added or removed manually. On the other hand, the goal of this scheduler is to improve
the integration of Hadoop and the applications that run on top of it with environments that
allow a more dynamic provisioning of resources.
> The current implementation is quite straightforward. Users specify a deadline at job
submission time, and the scheduler adjusts the resources to meet that deadline (at the moment,
the scheduler can be configured to either minimize or maximize the amount of resources). If
multiple jobs are run simultaneously, the scheduler prioritizes them by deadline. Note that
the current approach to estimate the completion time of jobs is quite simplistic: it is based
on the time it takes to finish each task, so it works well with regular jobs, but there is
still room for improvement for unpredictable jobs.
> The idea is to further integrate it with cloud-like and virtual environments (such as
Amazon EC2, Emotive, etc.) so that if, for instance, a job isn't able to meet its deadline,
the scheduler automatically requests more resources.

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