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

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dc.contributor.author Muhammad Munwar Iqbal
dc.contributor.author Muhammad Ali
dc.contributor.author Mai Alfawair
dc.contributor.author Ahsan Lateef
dc.contributor.author Abid Ali Minhas
dc.contributor.author Abdulaziz Al Mazyad
dc.contributor.author Kashif Naseer
dc.date.accessioned 2018-11-29T06:02:00Z
dc.date.available 2018-11-29T06:02:00Z
dc.date.issued 2018
dc.identifier.uri http://hdl.handle.net/123456789/7752
dc.description.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 homogeneousmobile cloud platformcomprising a laptop and virtualmachines 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 parallelismis achieved in proposed solution by using aMessage Passing Interface. Trace-based results and graphs will present the significance of the proposed method. en_US
dc.language.iso en en_US
dc.publisher Bahria University Islamabad Campus en_US
dc.relation.ispartofseries ;doi.org/10.1155/2018/4796535
dc.subject Department of Computer Engineering en_US
dc.title Augmenting High-Performance Mobile Cloud Computations for Big Data in AMBER en_US
dc.type Article en_US


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