Please use this identifier to cite or link to this item: https://idr.l4.nitk.ac.in/jspui/handle/123456789/9004
Title: Modified MapReduce framework for enhancing performance of graph based algorithms by fast convergence in distributed environment
Authors: Singhal, H.
Ram Mohana Reddy, Guddeti
Issue Date: 2014
Citation: Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014, 2014, Vol., , pp.1240-1245
Abstract: The amount of data which is produced is huge in current world and more importantly it is increasing exponentially. Traditional data storage and processing techniques are ineffective in handling such huge data [10]. Many real life applications require iterative computations in general and in particular used in most of machine learning and data mining algorithms over large datasets, such as web link structures and social network graphs. MapReduce is a software framework for easily writing applications which process large amount of data (multi-terabyte) in parallel on large clusters (thousands of nodes) of commodity hardware. However, because of batch oriented processing of MapReduce we are unable to utilize the benefits of MapReduce in iterative computations. Our proposed work is mainly focused on optimizing three factors resulting in performance improvement of iterative algorithms in MapReduce environment. In this paper, we address the key issues based on execution of tasks, the unnecessary creation of new task in each iteration and excessive shuffling of data in each iteration. Our preliminary experiments have shown promising results over the basic MapReduce framework. The comparative study with existing solutions based on MapReduce framework like HaLoop, has also shown better performance w.r.t algorithm run time and amount of data traffic over Hadoop Cluster. � 2014 IEEE.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/9004
Appears in Collections:2. Conference Papers

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