NDLI: Locality Based Data Partitioning in MapReduce

Content Provider	IEEE Xplore Digital Library
Author	Chunguang Wang Qingbo Wu Yusong Tan Wenzhu Wang Quanyuan Wu
Copyright Year	2013
Description	Author affiliation: Coll. of Comput., Nat. Univ. of Defense Technol., Changsha, China (Chunguang Wang; Qingbo Wu; Yusong Tan; Wenzhu Wang; Quanyuan Wu)
Abstract	Performance of MapReduce heavily depends on its data partitions for parallelism controlling, however, current state of art solutions are implemented using naive methods which are far from optimal. In this paper, we outline our solution, a locality based and skew aware partition technology, intend to address the data partition problems which encumber the job performance seriously. Our solution LBP(Locality Based Partitioning) clusters data blocks from a same node into a single partition, which need only one map task to process avoiding the spoil time for slot reallocation and multiple tasks initializing. Realizing the data skew problem, we enhance the LBP to LBP-SA(LBP Skew Aware) to partition the data file according their record and computation skews, so decrease the variety of tasks lifetime. Experiments results demonstrated that our solutions can improve the MapReduce processing performance remarkably than traditional Hadoop Implementation.
Starting Page	1310
Ending Page	1317
File Size	306738
Page Count	8
File Format	PDF
ISBN	9780769550961
DOI	10.1109/CSE.2013.194
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Navigation Data partition; Data Skew; MapReduce; Locality Based Partitioning Parallel processing Tin Peer-to-peer computing Partitioning algorithms Complexity theory Arrays
Content Type	Text
Resource Type	Article

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