Cloud Computing for Data-Intensive Applications / edited by Xiaolin Li, Judy Qiu.

Format:

Book

Contributor:

Li, Xiaolin, editor.

Qiu, Judy, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Computers.

Computer networks.

Application software.

Database management.

Information Systems and Communication Service.

Computer Communication Networks.

Information Systems Applications (incl. Internet).

Database Management.

Local Subjects:

Information Systems and Communication Service.

Computer Communication Networks.

Information Systems Applications (incl. Internet).

Database Management.

Physical Description:

1 online resource (VIII, 427 pages) : 180 illustrations

Edition:

First edition 2014.

Contained In:

Springer eBooks

Place of Publication:

New York, NY : Springer New York : Imprint: Springer, 2014.

System Details:

text file PDF

Summary:

This book presents a range of cloud computing platforms for data-intensive scientific applications. It covers systems that deliver infrastructure as a service, including: HPC as a service; virtual networks as a service; scalable and reliable storage; algorithms that manage vast cloud resources and applications runtime; and programming models that enable pragmatic programming and implementation toolkits for eScience applications. Many scientific applications in clouds are also introduced, such as bioinformatics, biology, weather forecasting and social networks. Most chapters include case studies. Cloud Computing for Data-Intensive Applications targets advanced-level students and researchers studying computer science and electrical engineering. Professionals working in cloud computing, networks, databases and more will also find this book useful as a reference.

Contents:

Scalable Deployment of a LIGO Physics Application on Public Clouds:Workflow Engine and Resource Provisioning Techniques

The FutureGrid Testbed for Big Data

Cloud Networking to Support Data Intensive Applications

IaaS cloud benchmarking: approaches, challenges, and experience

Adaptive Workload Partitioning and Allocation for Data Intensive Scientific Applications

Federating Advanced CyberInfrastructures with Autonomic Capabilities

Executing Storm Surge Ensembles on PAAS Cloud

Migrating Scientific Workflow Management Systems from the Grid to the Cloud

Efficient Task-Resource Matchmaking Using Self-Adaptive Combinatorial Auction

Cross-Phase Optimization in MapReduce

DRAW: A New Data-gRouping-AWare Data Placement Scheme for Data Intensive Applications with Interest Locality

Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation

GPU-Accelerated Cloud Computing Data-Intensive Applications

Big Data Storage and Processing on Azure Clouds: Experiments at Scale and Lessons Learned

Storage and Data Lifecycle Management in Cloud Environments with FRIEDA

DTaaS: Data Transfer as a Service in the Cloud

Supporting a Social Media Observatory with Customizable Index Structures - Architecture and Performance.

Other Format:

Printed edition:

ISBN:

978-1-4939-1905-5

9781493919055

Access Restriction:

Restricted for use by site license.