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Full Description
The amount of data in everyday life has been exploding. This data increase has been especially significant in scientific fields, where substantial amounts of data must be captured, communicated, aggregated, stored, and analyzed. Cloud Computing with e-Science Applications explains how cloud computing can improve data management in data-heavy fields such as bioinformatics, earth science, and computer science.
The book begins with an overview of cloud models supplied by the National Institute of Standards and Technology (NIST), and then:
Discusses the challenges imposed by big data on scientific data infrastructures, including security and trust issues
Covers vulnerabilities such as data theft or loss, privacy concerns, infected applications, threats in virtualization, and cross-virtual machine attack
Describes the implementation of workflows in clouds, proposing an architecture composed of two layers—platform and application
Details infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and software-as-a-service (SaaS) solutions based on public, private, and hybrid cloud computing models
Demonstrates how cloud computing aids in resource control, vertical and horizontal scalability, interoperability, and adaptive scheduling
Featuring significant contributions from research centers, universities, and industries worldwide, Cloud Computing with e-Science Applications presents innovative cloud migration methodologies applicable to a variety of fields where large data sets are produced. The book provides the scientific community with an essential reference for moving applications to the cloud.
Contents
Evaluation Criteria to Run Scientific Applications in the Cloud. Cloud-Based Infrastructure for Data-Intensive e-Science Applications: Requirements and Architecture. Securing Cloud Data. Adaptive Execution of Scientific Workflow Applications on Clouds. Migrating e-Science Applications to the Cloud: Methodology and Evaluation. Closing the Gap between Cloud Providers and Scientific Users. Assembling Cloud-Based Geographic Information Systems: A Pragmatic Approach Using Off-the-Shelf Components. HCloud, a Healthcare-Oriented Cloud System with Improved Efficiency in Biomedical Data Processing. RPig: Concise Programming Framework by Integrating R with Pig for Big Data Analytics. AutoDock Gateway for Molecular Docking Simulations in Cloud Systems. SaaS Clouds Supporting Biology and Medicine. Energy-Aware Policies in Ubiquitous Computing Facilities.
