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BioMed Research International
Volume 2018, Article ID 7501042, 17 pages
Research Article

Parallel MapReduce: Maximizing Cloud Resource Utilization and Performance Improvement Using Parallel Execution Strategies

1Department of Smart Computing, Kyungdong University, Global Campus, 46 4-gil, Gosung, Gangwondo 24764, Republic of Korea
2Faculty of Computer and Information Technology, Al-Madinah International University, 2 Jalan Tengku Ampuan Zabedah E/9E, 40100 Shah Alam, Selangor, Malaysia
3Department of Computer & Information Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Republic of Korea

Correspondence should be addressed to Dae-Ki Kang;

Received 12 April 2018; Accepted 30 September 2018; Published 17 October 2018

Academic Editor: Gerald J. Wyckoff

Copyright © 2018 Ahmed Abdulhakim Al-Absi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


MapReduce is the preferred cloud computing framework used in large data analysis and application processing. MapReduce frameworks currently in place suffer performance degradation due to the adoption of sequential processing approaches with little modification and thus exhibit underutilization of cloud resources. To overcome this drawback and reduce costs, we introduce a Parallel MapReduce () framework in this paper. We design a novel parallel execution strategy of Map and Reduce worker nodes. Our strategy enables further performance improvement and efficient utilization of cloud resources execution of Map and Reduce functions to utilize multicore environments available with computing nodes. We explain in detail makespan modeling and working principle of the framework in the paper. Performance of is compared with Hadoop through experiments considering three biomedical applications. Experiments conducted for BLAST, CAP3, and DeepBind biomedical applications report makespan time reduction of 38.92%, 18.00%, and 34.62% considering the framework against Hadoop framework. Experiments' results prove that the cloud computing platform proposed is robust, cost-effective, and scalable, which sufficiently supports diverse applications on public and private cloud platforms. Consequently, overall presentation and results indicate that there is good matching between theoretical makespan modeling presented and experimental values investigated.