International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Most Trusted Research Journal Since Year 2012

ISSN: 2319-7064

Research Paper | Computer Science & Engineering | India | Volume 3 Issue 8, August 2014

An Enhanced Approach for Resource Management Optimization in Hadoop

R. Sandeep Raj, G. Prabhakar Raju

Many tools and frameworks have been developed to process data on distributed data centers. MapReduce[3] most prominent among such frameworks has emerged as a popular distributed data processing model for processing vast amount of data in parallel on large clusters of commodity machines. The JobTracker in MapReduce framework is responsible for both managing the cluster's resources and executing the MapReduce jobs ,a constraint that limits scalability ,resource utilization. YARN[2] the next-generation execution layer for Hadoop splits processing and resource management capabilities of JobTracker into separate entities and eliminates the dependency of Hadoop on MapReduce.This new model is more isolated and scalable compared to MapReduce,providing improved features and functionality. This paper discusses the design of YARN and significant advantages over traditional MapReduce.

Keywords: BigData, Hadoop, YARN, MapReduce

Edition: Volume 3 Issue 8, August 2014

Pages: 1248 - 1253


How to Cite this Article?

R. Sandeep Raj, G. Prabhakar Raju, "An Enhanced Approach for Resource Management Optimization in Hadoop", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=2015669, Volume 3 Issue 8, August 2014, 1248 - 1253

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