Title

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

Authors

Authors

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

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

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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