Keywords:
Science--Data processing.
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Computational grids (Computer systems).
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Research--Data processing.
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Electronic books.
Description / Table of Contents:
Using real-world examples, this book shows how computational technologies and tools can be used to build essential infrastructures supporting next-generation scientific research. Covers security, privacy, collaboration, automated workflow technology and more.
Type of Medium:
Online Resource
Pages:
1 online resource (553 pages)
Edition:
1st ed.
ISBN:
9780857294395
Series Statement:
Computer Communications and Networks Series
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=763220
DDC:
501/.13
Language:
English
Note:
Intro -- Guide to e-Science -- Foreword -- Preface -- Part I: Sharing and Open Research -- Part II: Data-Intensive e-Science -- Part III: Collaborative Research -- Part IV: Research Automation, Reusability, Reproducibility and Repeatability -- Part V: e-Science, Easy Science -- Acknowledgements -- Contents -- About the Editors -- Contributors -- Part I: Sharing and Open Research -- Chapter 1: Implementing a Grid/Cloud eScience Infrastructure for Hydrological Sciences -- 1.1 Introduction: eScience, Grid, and Cloud Computing -- 1.1.1 Grid Computing for Environmental Sciences -- 1.1.2 The Objective of This Chapter -- 1.2 Building an eScience Infrastructure -- 1.2.1 Computing Grids -- 1.2.1.1 Local Batching Systems -- 1.2.1.2 Globus Toolkit -- 1.2.1.3 Remote My_Condor_Submit (RMCS) -- 1.2.2 Data Grids -- 1.2.2.1 Data Access: Storage Resources Broker (SRB) -- 1.2.2.2 Metadata Access: Rcommands -- 1.2.3 Integrating EC2 to the Grids with RMCS -- 1.3 Geographic Information Visualization -- 1.3.1 WKML Functions -- 1.4 Case Study: Running a Hydrological Application SHETRAN -- 1.4.1 Grid-Enabling SHETRAN -- 1.4.1.1 First Stage: Data Preparation -- 1.4.1.2 Second Stage: XMLization -- 1.4.1.3 Third Stage: Run the Simulation Jobs in Grids -- 1.4.1.4 Final Stage: Query/Filtering Outputs for Data Analysis -- 1.5 Discussions -- References -- Chapter 2: The German Grid Initiative D-Grid: Current State and Future Perspectives -- 2.1 Introduction -- 2.2 History of D-Grid -- 2.3 Organizational Structure -- 2.3.1 The D-Grid gGmbH -- 2.3.2 The D-Grid Integration Project -- 2.4 Selected User Communities -- 2.4.1 AstroGrid-D -- 2.4.2 Geospatial Data Processing -- 2.5 The D-Grid Infrastructure -- 2.5.1 Central Services -- 2.5.2 D-Grid Reference System -- 2.6 Challenges and Prospects of a Future D-Grid -- 2.6.1 Cloud Computing -- 2.6.1.1 Authentication.
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2.6.1.2 User Management -- 2.6.1.3 Information System -- 2.6.1.4 Accounting -- 2.6.2 Quality-Of-Service Guarantees Through Service Level Agreements -- 2.6.2.1 Requirements for Service Level Agreements in D-Grid -- Standard Protocols and Demand for (Negotiation) -- Support for Different Application Domains with Individual Vocabularies -- Extensibility and Revision of SLA Templates -- Orchestration of Domain-Specific and Generic Services -- Scalability -- Assessment of Agreements -- Integration with D-Grid Central Services -- 2.6.2.2 Technological Foundations -- 2.6.2.3 A Service Level Agreement Layer for D-Grid -- 2.6.3 D-Grid and NGI-DE: Toward a Unified German Grid -- 2.7 Outlook -- References -- Chapter 3: Democratizing Resource-Intensive e-Science Through Peer-to-Peer Grid Computing -- 3.1 Introduction -- 3.2 A Peer-to-Peer Approach for Grid Computing -- 3.3 Building a P2P Grid -- 3.3.1 The Network of Favors -- 3.3.2 Accounting Computation Performed by Untrustworthy Parties -- 3.3.3 Reciprocity in Face of Multiple Services -- 3.4 Supporting the Execution of Resource-Intensive Applications -- 3.4.1 Exposing and Discovering Resources -- 3.4.2 Scheduling BoT Jobs in a P2P Grid -- 3.4.3 Caching Data -- 3.4.4 Security Issues -- 3.4.4.1 Protecting Workers from Malicious Jobs -- 3.4.4.2 Protecting the Jobs from Malicious Workers -- 3.4.4.3 Securing Peer Identities -- 3.5 Cooperating and Coexisting with Other Distributed Computing Infrastructures -- 3.6 A Success Story: The SegHidro Project -- 3.7 Conclusion -- 3.7.1 Lessons Learned -- 3.7.2 Present Challenges and Future Directions -- References -- Chapter 4: Peer4Peer: e-Science Community for Network Overlay and Grid Computing Research -- 4.1 Introduction -- 4.2 Current Approaches to e-Science -- 4.3 The Need for Next Generation Overlay and Grid Simulation.
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4.4 The Peer4Peer Vision: Peer-to-Peer Cycle-Sharing, Parallelized Simulation, Data and Protocol Language -- 4.5 Current Peer4Peer Architecture -- 4.5.1 Mesh: Cycle-Sharing Overlay -- 4.5.2 Simulator: Parallel and Distributed Topology Simulation -- 4.5.3 High-Level Services -- 4.5.4 Topology Modeling -- 4.5.5 Integration and Portal Support -- 4.6 Implementation -- 4.6.1 Nuboinc: Peer4Peer Portal -- 4.6.2 STARC: Resource Requirement Descriptions -- 4.6.3 Ginger: Peer-to-Peer Discovery Cycle-Sharing, and Work Distribution -- 4.6.4 P4PSim: Scalable and Efficient Peer-to-Peer Simulation -- 4.6.5 Other Issues: Security, Heterogeneity -- 4.7 Evaluation -- 4.7.1 Resource Discovery and Gridlet Routing in Ginger -- 4.7.2 Parallel Simulation in P4PSim -- 4.8 Related Work -- 4.8.1 Simulation Tools and Test Beds -- 4.8.2 Parallel and Distributed Computing -- 4.8.3 Cycle-Providing Infrastructures -- 4.8.4 Peer-to-Peer Overlays -- 4.8.5 Grid Middleware -- 4.9 Conclusion -- References -- Part II: Data-Intensive e-Science -- Chapter 5: A Multidisciplinary, Model-Driven, Distributed Science Data System Architecture -- 5.1 Introduction -- 5.2 Applying e-science Principles to Science -- 5.3 The Architectural Model and Framework -- 5.4 An Information-Centric Approach -- 5.5 The Planetary Science Model -- 5.6 Earth Science Research -- 5.7 Cancer Research -- 5.8 Related Work -- 5.9 Conclusion -- References -- Chapter 6: Galaxy: A Gateway to Tools in e-Science -- 6.1 Introduction -- 6.2 Galaxy: A Tool Integration Framework -- 6.2.1 Galaxy Goals -- 6.2.2 Galaxy Components -- 6.2.2.1 Data Analysis -- 6.2.2.2 Data Sharing -- 6.2.2.3 Data Acquisition and Visualization -- 6.2.2.4 Access to Computational Resources -- 6.2.3 Galaxy Architecture -- 6.2.3.1 Implementation Details -- 6.2.3.2 Software Engineering Details -- 6.3 Deploying and Customizing Galaxy.
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6.3.1 The Installation Process -- 6.3.2 Adding Tools to Galaxy -- 6.3.3 Customizing Galaxy -- 6.3.4 Galaxy Accessibility -- 6.3.5 Galaxy Usage Example -- 6.4 Enabling the Next Step in e-Science -- 6.4.1 Galaxy and IaaS -- 6.4.2 Galaxy and AWS -- 6.4.2.1 GC Implementation -- 6.4.2.2 Interacting with GC -- 6.5 Related Work -- 6.6 Conclusions -- References -- Chapter 7: An Integrated Ontology Management and Data Sharing Framework for Large-Scale Cyberinfrastructure -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Ontology Management Systems -- 7.2.2 Data Sharing Systems -- 7.3 The Proposed Framework -- 7.3.1 System Architecture -- 7.3.2 Ontology Management -- 7.3.2.1 Conceptual Schema -- 7.3.2.2 Object-Oriented Ontology Schema -- 7.3.2.3 Ontology Creation and Publishing -- 7.3.2.4 Ontology Search and Discovery -- 7.3.3 Semantic Data Access -- 7.3.3.1 Query and Data Discovery -- 7.3.3.2 Ontology Mapped Data Access -- 7.3.3.3 Data Conversion -- 7.4 Implementation -- 7.4.1 Case Studies -- 7.4.2 Interoperability Challenges -- 7.4.3 Tools and Services -- 7.5 Conclusions -- References -- Part III: Collaborative Research -- Chapter 8: An e-Science Cyberinfrastructure for Solar-Enabled Water Production and Recycling -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Solar-Enabled Water Production and Recycling -- 8.2.1.1 System Overview -- 8.2.1.2 System Architecture -- 8.2.2 e-Science Cyberinfrastructure -- 8.2.2.1 Motivation -- 8.2.2.2 Smart e-Science Cyberinfrastructure -- 8.3 Cyberinfrastructure Design for Water Production and Recycling -- 8.3.1 System Architecture and Components -- 8.3.2 Ontology Design -- 8.3.3 Data Management -- 8.3.3.1 Data Preprocessing Service -- 8.3.3.2 Data Cleaning Service -- 8.3.3.3 Data Alignment and Interpolation Service -- 8.3.4 Workflow Management -- 8.3.4.1 Workflow Tools and Services for Solar Radiation Map.
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8.3.4.2 Workflow for Data Mining and Control -- 8.4 Implementation -- 8.4.1 Data Acquisition and Archiving -- 8.4.2 Ontology Implementation -- 8.4.3 Implementation of a Visualization Engine for Solar Radiation Map -- 8.4.3.1 Query Engine -- 8.4.3.2 Visualization and Geospatial Engine -- 8.4.3.3 Web Interface -- 8.4.3.4 Solar Radiation Mapping Services -- 8.5 Conclusion -- References -- Chapter 9: e-Science Infrastructure Interoperability Guide: The Seven Steps Toward Interoperability for e-Science -- 9.1 Introduction and Overview -- 9.2 Motivation and Relevance -- 9.3 An Emerging Design Pattern in e-Science -- 9.3.1 State-of-the-Art e-Science Infrastructures -- 9.3.2 Algorithm Using Different Computational Paradigms -- 9.3.3 e-Science Infrastructure Interoperability Approaches -- 9.3.3.1 Reference Models That Promote Interoperability -- 9.3.3.2 Component-Based Approaches to Enable Interoperability -- 9.3.4 Evaluation of Interoperability Benefits as a Key Challenge -- 9.4 The Seven Steps Toward Interoperable e-Science Infrastructures -- 9.4.1 Step 1: Open Standard-Based Reference Model -- 9.4.1.1 Guiding Principles of Reference Models -- 9.4.1.2 Follow an Open Standard-Driven Design Approach -- 9.4.2 Step 2: Collaboration with the Right Set of Vendors -- 9.4.2.1 Seek First to Understand Than to Be Understood -- 9.4.3 Step 3: Reference Implementations -- 9.4.3.1 Include Relationships and Identify Missing Links -- 9.4.4 Step 4: Standardization Feedback Ecosystem -- 9.4.5 Step 5: Aligned Future Strategies and Roadmaps -- 9.4.6 Step 6: Harmonized Operation Policies -- 9.4.7 Step 7: Funding Sources and Cross-Project Coordination -- 9.5 Conclusions and Summary -- References -- Chapter 10: Trustworthy Distributed Systems Through Integrity-Reporting -- 10.1 Introduction -- 10.2 Motivating Examples -- 10.2.1 climateprediction.net and Condor Grids.
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10.2.2 Healthcare Grids.
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