Title
Augmenting High-Performance Mobile Cloud Computations for Big Data in AMBER.
Abstract
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 databased 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.
Year
DOI
Venue
2018
10.1155/2018/4796535
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
Field
DocType
Volume
Mobile cloud computing,Complex system,Virtual machine,Laptop,Computer science,Model building,Message Passing Interface,Big data,Distributed computing,Cloud computing
Journal
2018
ISSN
Citations 
PageRank 
1530-8669
0
0.34
References 
Authors
8
7
Name
Order
Citations
PageRank
Muhammad Munwar Iqbal153.80
Muhammad Ali211022.83
M. Alfawair362.21
Ahsan Lateef400.34
Abid Ali Minhas5437.53
Abdulaziz Almazyad610.70
Kashif Naseer730.76