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From "Ming Chen (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MAPREDUCE-5605) Memory-centric MapReduce aiming to solve the I/O bottleneck
Date Mon, 04 Nov 2013 06:55:33 GMT

     [ https://issues.apache.org/jira/browse/MAPREDUCE-5605?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

Ming Chen updated MAPREDUCE-5605:

    Attachment:     (was: ReduceTaskRunner.java)

> 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
> jdk7 preferred
>            Reporter: Ming Chen
>            Assignee: Ming Chen
>         Attachments: ReinitTrackerAction.java, RoundQueue.java, RunningJob.java, SequenceFileOutputFormat.java,
SpillScheduler.java, Task.java
> 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. We have conducted extensive
experiments with comparison against the native Hadoop platform. The results show that the
Mammoth system can reduce the job execution time by more than 40% in typical cases, without
requiring any modifications of the Hadoop programs. When a system is short of memory, Mammoth
can improve the performance by up to 4 times, as observed for I/O intensive applications,
such as PageRank. 

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