DRAW: A New Data-gRouping-AWare Data Placement Scheme for Data Intensive Applications With Interest Locality

    Authors

    J. Wang; Q. J. Xiao; J. L. Yin;P. J. Shang

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Magn.

    Keywords

    Data intensive; data layout; Hadoop; MapReduce; Engineering, Electrical & Electronic; Physics, Applied

    Abstract

    Recent years have seen an increasing number of scientists employ data parallel computing frameworks such as MapReduce and Hadoop to run data intensive applications and conduct analysis. In these co-located compute and storage frameworks, a wise data placement scheme can significantly improve the performance. Existing data parallel frameworks, e. g., Hadoop, or Hadoop-based clouds, distribute the data using a random placement method for simplicity and load balance. However, we observe that many data intensive applications exhibit interest locality which only sweep part of a big data set. The data often accessed together result from their grouping semantics. Without taking data grouping into consideration, the random placement does not perform well and is way below the efficiency of optimal data distribution. In this paper, we develop a new Data-gRouping-AWare (DRAW) data placement scheme to address the above-mentioned problem. DRAW dynamically scrutinizes data access from system log files. It extracts optimal data groupings and re-organizes data layouts to achieve the maximum parallelism per group subjective to load balance. By experimenting two real-world MapReduce applications with different data placement schemes on a 40-node test bed, we conclude that DRAW increases the total number of local map tasks executed up to 59.8%, reduces the completion latency of the map phase up to 41.7%, and improves the overall performance by 36.4%, in comparison with Hadoop's default random placement.

    Journal Title

    Ieee Transactions on Magnetics

    Volume

    49

    Issue/Number

    6

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    2514

    Last Page

    2520

    WOS Identifier

    WOS:000319977400018

    ISSN

    0018-9464

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