Recent years it has been seen that increasing number of scientists have studied and analyzed data parallel computing frameworks such as MapReduce and Hadoop to run data intensive applications. In this computing environment we 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-g Rouping-A Ware (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 Map Reduce 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.

Author Name: Vaibhav M. Palve, Prachi Singhai

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Keywords: Data intensive, data layout, Hadoop, MapReduce.

ISSN: 2394-9007

EISSN: 0000-0000

EOI/DOI: Volume-V, Number-III, June 201

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