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https://issues.apache.org/jira/browse/MAPREDUCE-5605?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xuanhua Shi reassigned MAPREDUCE-5605:
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Assignee: Xuanhua Shi (was: Ming Chen)
> 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: Xuanhua Shi
> Fix For: 1.0.1
>
> Attachments: MAPREDUCE-5605-v1.patch,
> 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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