The Parallel Processing Techniques in Mobile Cloud Computing for Enhanced Big Data Computation in AMBER

Mobile Cloud Computing for Enhanced Big Data

Abstract

Big data is developing quickly, combining industrial techniques and scholarly research to address the difficulties of organising and comprehending large-scale datasets. This dissertation investigates how the AMBER software's molecular dynamics simulations can benefit from the increased processing power provided by High-Performance Mobile Cloud Computing (HPMCC). Fortunately, demonstrates a way to more effectively divide computational demands through parallel processing with the Message Passing Interface (MPI) by establishing connections between laptops and virtual machines over a mobile cloud infrastructure. This approach provides a scalable and affordable replacement for conventional supercomputers and speeds up processing. Furthermore, we tackle security issues with Single Sign-On (SSO) technologies, showcasing cloud computing's capacity to manage massive data demands, especially in scientific research.