Librería Portfolio

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.



Preface

Acknowledgments

About the Authors

List of Contributors

Evaluation Criteria to Run Scientific Applications in the Cloud; Eduardo Roloff, Alexandre da Silva Carissimi, and Philippe Olivier Alexandre Navaux

Cloud-Based Infrastructure for Data-Intensive e-Science Applications: Requirements and Architecture; Yuri Demchenko, Canh Ngo, Paola Grosso, Cees de Laat, and Peter Membrey

Securing Cloud Data; Sushmita Ruj and Rajat Saxena

Adaptive Execution of Scientific Workflow Applications on Clouds; Rodrigo N. Calheiros, Henry Kasim, Terence Hung, Xiaorong Li, Sifei Lu, Long Wang, Henry Palit, Gary Lee, Tuan Ngo, and Rajkumar Buyya

Migrating e-Science Applications to the Cloud: Methodology and Evaluation; Steve Strauch, Vasilios Andrikopoulos, Dimka Karastoyanova, and Karolina Vukojevic-Haupt

Closing the Gap between Cloud Providers and Scientific Users; David Susa, Harold Castro, and Mario Villamizar

Assembling Cloud-Based Geographic Information Systems: A Pragmatic Approach Using Off-the-Shelf Components; Muhammad Akmal, Ian Allison, and Horacio González-Vélez

HCloud, a Healthcare-Oriented Cloud System with Improved Efficiency in Biomedical Data Processing; Ye Li, Chenguang He, Xiaomao Fan, Xucan Huang, and Yunpeng Cai

RPig: Concise Programming Framework by Integrating R with Pig for Big Data Analytics; MingXue Wang and Sidath B. Handurukande

AutoDock Gateway for Molecular Docking Simulations in Cloud Systems; Zoltán Farkas, Péter Kacsuk, Tamás Kiss, Péter Borsody, Ákos Hajnal, Ákos Balaskó, and Krisztián Karóczkai

SaaS Clouds Supporting Biology and Medicine; Philip Church, Andrzej Goscinski, Adam Wong, and Zahir Tari

Energy-Aware Policies in Ubiquitous Computing Facilities; Marina Zapater, Patricia Arroba, José Luis Ayala Rodrigo, Katzalin Olcoz Herrero, and José Manuel Moya Fernandez

Index