Implementation of Effective Data Emplacement Algorithm in Heterogeneous Cloud EnvironmentAuthor : S. Annapoorani and B. Srinivasan
Volume 8 No.1 Special Issue:February 2019 pp 87-88
This paper is concerned with the study and implementation of effective Data Emplacement Algorithm in large set of databases called Big Data and proposes a model for improving the efficiency of data processing and storage utilization for dynamic load imbalance among nodes in a heterogeneous cloud environment. With the era of explosive information and data receiving, more and more fields need to deal with massive, large scale of data. A method has been proposed with an improved Data Placement algorithm called Effective Data Emplacement Algorithm with computing capacity of each node as a predominant factor that promotes and improves the efficiency in data processing in a short duration time from large set of data. The adaptability of the proposed model can be obtained by minimizing the time with processing efficiency through the computing capacity of each node in the cluster. The proposed solution improves the performance of the heterogeneous cluster environment by effectively distributing data based on the performance oriented sampling as the experimental results made with word count applications.
Big Data, Cloud computing, Hadoop, Data Emplacement, Cluster
 Jiong Xie, Shu Yin, Xiaojun Ruan, Zhiyang Ding and Yun Tian, “Improving MapReduce Performance through Data Placement in Heterogeneous Hadoop Clusters”, in 19th International Heterogeneity in Computing Workshop, Atlanta, Georgia, April 2010.
 Yuanquan Fan, Weiguo Wu, Haijun Cao, Huo Zhu, Xu Zhao and Wei Wei, “A heterogeneity-aware data distribution and rebalance method in Hadoop cluster”, in Seventh ChinaGrid Annual Conference, 2012.
 Mahesh Maurya and Sunita Mahajan, “Performance analysis of MapReduce Programs on Hadoop Cluster”, IEEE World Congress on Information and Communication technologies, 2012.
 Wentao Zhao, Lingjun Meng, Jiangfeng Sun and Yang Ding, “An Improved Data Placement Strategy in a Heterogeneous Hadoop Cluster”, The Open Cybernetics & Systemics Journal, 2014.
 Chia-Wei Lee, Kuang-Yu Hsieh, Sun-Yuan Hsieh and Hung-Chang Hsiao, “A Dynamic Data Placement Strategy for Hadoop in Heterogeneous Environments”, Big Data Research, 2014.
 Suhas V. Ambade and Priya R. Deshpande, “Heterogenity-based files placement in Big Data Cluster”, in International Conference on Computational Intelligence and Communication Networks, 2015.
 Vrushali Ubarhande, “Novel Data-Distribution Technique for Hadoop in Heterogeneous Cloud Environments”, IEEE Transactions 2015.
 Ch. Bhaskar VishnuVardhan and Pallav Kumar Baruah, “Improving the Performance of Heterogeneous Hadoop Cluster”, in Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), 2016.