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Wireless Communications and Mobile Computing
Volume 2018 (2018), Article ID 4796535, 12 pages
Research Article

Augmenting High-Performance Mobile Cloud Computations for Big Data in AMBER

1Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
2Prince Abdullah Bin Ghazi Faculty of Information Technology, Al-Balqa’ Applied University, Salt, Jordan
3Department of Computer Science, University of Agriculture, Faisalabad, Pakistan
4College of Computer and Information Systems, Al Yamamah University, Riyadh, Saudi Arabia
5King Saud University, Riyadh, Saudi Arabia
6Department of Computer Engineering, Bahria University, Islamabad, Pakistan

Correspondence should be addressed to Abid Ali Minhas;

Received 30 August 2017; Accepted 30 January 2018; Published 2 April 2018

Academic Editor: Syed H. Ahmed

Copyright © 2018 Muhammad Munwar Iqbal 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.


Big data is an inspirational area of research that involves best practices used in the industry and academia. Challenging and complex systems are the core requirements for the data collation and analysis of big data. Data analysis approaches and algorithms development are the necessary and essential components of the big data analytics. Big data and high-performance computing emergent nature help to solve complex and challenging problems. High-Performance Mobile Cloud Computing (HPMCC) technology contributes to the execution of the intensive computational application at any location independently on laptops using virtual machines. HPMCC technique enables executing computationally extreme scientific tasks on a cloud comprising laptops. Assisted Model Building with Energy Refinement (AMBER) with the force fields calculations for molecular dynamics is a computationally hungry task that requires high and computational hardware resources for execution. The core objective of the study is to deliver and provide researchers with a mobile cloud of laptops capable of doing the heavy processing. An innovative execution of AMBER with force field empirical formula using Message Passing Interface (MPI) infrastructure on HPMCC is proposed. It is homogeneous mobile cloud platform comprising a laptop and virtual machines as processors nodes along with dynamic parallelism. Some processes can be executed to distribute and run the task among the various computational nodes. This task-based and data-based parallelism is achieved in proposed solution by using a Message Passing Interface. Trace-based results and graphs will present the significance of the proposed method.