CLOUD COMPUTING FOR DATA-DRIVEN SCIENCE AND ENGINEERING: A COMPREHENSIVE REVIEW
Keywords:
Cloud Computing, Data-Driven Science, Big Data, Scientific Research, Computational ResourcesAbstract
In recent years, data-driven science and engineering have become pivotal in advancing our understanding of the world and solving complex problems. The advent of sophisticated instruments like next-generation genome sequencers, gigapixel survey telescopes, and extensive sensor networks has resulted in unprecedented growth in data volume and complexity. This surge in data has necessitated the development of new methodologies and computational platforms capable of managing, processing, and analyzing massive datasets effectively. Cloud computing has emerged as a transformative technology in this landscape, offering scalable, flexible, and cost-effective solutions for handling Big Data. By providing on-demand access to computational resources and storage, cloud platforms enable scientists and engineers to perform high-level data analysis and simulations that were previously infeasible with traditional computing infrastructures (Birant & Birant, 2020; [Simmhan et al., 2016). This review article explores the pivotal role of cloud computing in facilitating data-driven science and engineering. It delves into the historical background and evolution of cloud computing in scientific research, outlines key concepts and technologies, and highlights significant applications across various scientific disciplines. Additionally, the article discusses the benefits and challenges associated with using cloud computing for Big Data applications and presents future trends and potential advancements in this rapidly evolving field.
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Copyright (c) 2024 Sivanagaraju Gadiparthi , Harshavardhan Nerella (Author)
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