Title

Draw: A New Data-Grouping-Aware Data Placement Scheme For Data Intensive Applications With Interest Locality

Keywords

Data intensive; data layout; Hadoop; MapReduce

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. © 1965-2012 IEEE.

Publication Date

6-14-2013

Publication Title

IEEE Transactions on Magnetics

Volume

49

Issue

6

Number of Pages

2514-2520

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TMAG.2013.2251613

Socpus ID

84878770725 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84878770725

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