Cloud Computing with e-Science Applications

"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. This book describes how cloud computing can significantly improve data management in different scientific fields, such as bioinformatics, earth science, and computer science. This book focuses on how cloud computing helps in the areas of resource control, vertical and horizontal scalability, and adaptive scheduling"-- Provided by publisher 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. Front Cover 1 Contents 7 Preface 9 Acknowledgments 15 About the Editors 17 List of Contributors 19 Chapter 1: Evaluation Criteria to Run Scientific Applications in the Cloud 23 Chapter 2: Cloud-Based Infrastructure for Data-Intensive e-Science Applications: Requirements and Architecture 39 Chapter 3: Securing Cloud Data 63 Chapter 4: Adaptive Execution of Scientific Workflow Applications on Clouds 95 Chapter 5: Migrating e-Science Applications to the Cloud: Methodology and Evaluation 111 Chapter 6: Closing the Gap between Cloud Providers and Scientific Users 137 Chapter 7: Assembling Cloud-Based Geographic Information Systems: A Pragmatic Approach Using Off-the-Shelf Components 163 Chapter 8: HCloud, a Healthcare-Oriented Cloud System with Improved Efficiency in Biomedical Data Processing 185 Chapter 9: RPig: Concise Programming Framework by Integrating R with Pig for Big Data Analytics 215 Chapter 10: AutoDock Gateway for Molecular Docking Simulations in Cloud Systems 239 Chapter 11: SaaS Clouds Supporting Biology and Medicine 259 Chapter 12: Energy-Aware Policies in Ubiquitous Computing Facilities 289 Back Cover 309 PrefaceAcknowledgmentsAbout the AuthorsList of ContributorsEvaluation Criteria to Run Scientific Applications in the Cloud; Eduardo Roloff, Alexandre da Silva Carissimi, and Philippe Olivier Alexandre NavauxCloud-Based Infrastructure for Data-Intensive e-Science Applications: Requirements and Architecture; Yuri Demchenko, Canh Ngo, Paola Grosso, Cees de Laat, and Peter MembreySecuring Cloud Data; Sushmita Ruj and Rajat SaxenaAdaptive Execution of Scientific Workflow Applications on Clouds; Rodrigo N. Calheiros, Henry Kasim, Terence Hung, Xiaorong Li, Sifei Lu, Long Wang, Henry Palit, Gary Lee, ..