Cloud Computing with e-Science Applications

Book Details

• Publisher: CRC Press

• Author: Olivier Terzo

• Language: English

• Edition: 1st Edition

• ISBN: 9780367738532

• Pages: 320

• Cover: Paperback, Import

About The Book

Cloud Computing with e-Science Applications by Olivier Terzo, published by CRC Press in 2020, explores the revolutionary impact of cloud computing on the management of data in scientific fields. As the amount of data in everyday life continues to skyrocket, particularly in data-heavy areas such as bioinformatics, earth science, and computer science, this book provides an in-depth understanding of how cloud computing can streamline data management and processing in these domains.

The book begins with an overview of cloud models from the National Institute of Standards and Technology (NIST) and delves into the challenges that big data poses to scientific data infrastructures. Key topics include security and trust issues, vulnerabilities such as data theft, privacy concerns, threats in virtualization, and cross-virtual machine attacks.

Terzo also describes the implementation of workflows in cloud environments, proposing a two-layer architecture consisting of the platform and application layers. The text covers essential cloud computing models, including Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS), and examines solutions based on public, private, and hybrid cloud computing models.

With contributions from research centers, universities, and industries around the world, Cloud Computing with e-Science Applications demonstrates how cloud computing aids in resource control, scalability, interoperability, and adaptive scheduling. This book is an essential reference for the scientific community, offering valuable insights into cloud migration methodologies and practical guidance for moving applications to the cloud in fields that generate large datasets.

This 320-page paperback edition is ideal for professionals, researchers, and students in data-intensive fields looking to understand and leverage cloud computing technologies for scientific data management.