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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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Ming Chen updated MAPREDUCE-5605:
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Attachment: (was: TaskLogAppender.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: ReduceTask.java, ReduceTaskRunner.java,
> ReduceTaskStatus.java, 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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