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  1. Hadoop Map/Reduce
  2. MAPREDUCE-5605

Memory-centric MapReduce aiming to solve the I/O bottleneck

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    Details

    • Type: Improvement
    • Status: Patch Available
    • Priority: Major
    • Resolution: Unresolved
    • Affects Version/s: 1.0.1
    • Fix Version/s: 1.0.1
    • Component/s: None
    • Labels:
    • Environment:

      x86-64 Linux/Unix
      64-bit jdk7 preferred

    • Tags:
      memory-centric multi-thread optimization task

      Description

      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.

        Attachments

        1. MAPREDUCE-5605-v1.patch
          250 kB
          Ming Chen
        2. hadoop-core-1.0.1-mammoth-0.9.0.jar
          3.81 MB
          Ming Chen
        3. TR-mammoth-HUST.pdf
          385 kB
          Ming Chen

          Activity

            People

            • Assignee:
              mammothcm Ming Chen
              Reporter:
              mammothcm Ming Chen
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              Dates

              • Created:
                Updated: