Book description
This book presents innovative cloud migration methodologies applicable to a variety of fields where large data sets are produced. It discusses challenges imposed by big data on scientific data infrastructures, including security and trust issues. It covers vulnerabilities such as data theft or loss, privacy concerns, infected applications, threats in virtualization, and cross-virtual machine attack. It details IaaS, PaaS, and SaaS solutions based on public, private, and hybrid models, demonstrating how cloud computing aids in resource control, vertical and horizontal scalability, interoperability, and adaptive scheduling.
Table of contents
- Cover Page
- Half Title
- Title Page
- Copyright
- Contents
- Preface
- Acknowledgments
- About the Editors
- List of Contributors
- 1 Evaluation Criteria to Run Scientific Applications in the Cloud
- 2 Cloud-Based Infrastructure for Data-Intensive e-Science Applications: Requirements and Architecture
- 3 Securing Cloud Data
- 4 Adaptive Execution of Scientific Workflow Applications on Clouds
- 5 Migrating e-Science Applications to the Cloud: Methodology and Evaluation
- 6 Closing the Gap between Cloud Providers and Scientific Users
- 7 Assembling Cloud-Based Geographic Information Systems: A Pragmatic Approach Using Off-the-Shelf Components
- 8 HCloud, a Healthcare-Oriented Cloud System with Improved Efficiency in Biomedical Data Processing
- 9 RPig: Concise Programming Framework by Integrating R with Pig for Big Data Analytics
- 10 AutoDock Gateway for Molecular Docking Simulations in Cloud Systems
- 11 SaaS Clouds Supporting Biology and Medicine
- 12 Energy-Aware Policies in Ubiquitous Computing Facilities
- Index
Product information
- Title: Cloud Computing with e-Science Applications
- Author(s):
- Release date: December 2017
- Publisher(s): CRC Press
- ISBN: 9781351831543
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