Title |
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Geo-Distributed Map Reduce Framework for Cost Efficient Big Data Analysis |
Authors |
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RA Nagarajan, A Muthumari |
Keywords |
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Geographical Distributed Servers, MapReduce, Hadoop, Big Data Analytics, Cost Efficient. |
Issue Date |
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October 2016 |
Abstract |
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Big data analysis is one of the major challenges of current era. The limits to what can be done are often times due to how much data can be processed in a given time-frame Implementation of map reduce framework in Hadoop plays an important role in handling and processing big data. Hence we concentrate on geographical distribution of geo-distributed data for sequential execution of map reduce jobs to optimize the execution time. Our paper introduces Location based job execution system, a system for efficiently processing geo-distributed big data. Geo-MapReduce is a Hadoop based framework that can efficiently perform a sequence of MapReduce jobs on a geo-distributed dataset across multiple datacenters. It act much like the atmosphere surrounding the clouds. The problem of executing geo-distributed MapReduce job sequences as arising in “cloud-of-clouds” scenarios is analyzed for job execution. For distributing the input data, DTG algorithm is applied to identify optimized execution path. The datacenter with optimized execution path is selected for job execution. The execution paths for performing a MapReduce job on a geo-distributed dataset are copy, geo and multi. Our system uses Multi execution method for efficient and optimized execution. |
Page(s) |
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1-5 |
ISSN |
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2347- 4734 |
Source |
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Vol. 3, No.5, October 2016 |
Download |
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