[jira] [Commented] (MAPREDUCE-5605) Memory-centric MapReduce aiming to solve the I/O bottleneck

["Hadoop QA (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAPREDUCE-5605?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13978236#comment-13978236 ] 

Hadoop QA commented on MAPREDUCE-5605:
--------------------------------------

{color:red}-1 overall{color}.  Here are the results of testing the latest attachment 
  http://issues.apache.org/jira/secure/attachment/12641477/TR-mammoth-HUST.pdf
  against trunk revision .

    {color:red}-1 patch{color}.  The patch command could not apply the patch.

Console output: https://builds.apache.org/job/PreCommit-MAPREDUCE-Build/4549//console

This message is automatically generated.

> Memory-centric MapReduce aiming to solve the I/O bottleneck
> -----------------------------------------------------------
>
>                 Key: MAPREDUCE-5605
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-5605
>             Project: Hadoop Map/Reduce
>          Issue Type: Improvement
>    Affects Versions: 1.0.1
>         Environment: x86-64 Linux/Unix
> 64-bit jdk7 preferred
>            Reporter: Ming Chen
>            Assignee: Ming Chen
>             Fix For: 1.0.1
>
>         Attachments: MAPREDUCE-5605-v1.patch, TR-mammoth-HUST.pdf, hadoop-core-1.0.1-mammoth-0.9.0.jar
>
>
> Memory is a very important resource to bridge the gap between CPUs and I/O devices. So the idea is to maximize the usage of memory to solve the problem of I/O bottleneck. We developed a multi-threaded task execution engine, which runs in a single JVM on a node. In the execution engine, we have implemented the algorithm of memory scheduling to realize global memory management, based on which we further developed the techniques such as sequential disk accessing, multi-cache and solved the problem of full garbage collection in the JVM. The benchmark results shows that it can get impressive improvement in typical cases. When the a system is relatively short of memory (eg, HPC, small- and medium-size enterprises), the improvement will be even more impressive.



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