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Cloud Computing for Data-Intensive Applications

  • Book
  • © 2014

Overview

Editors:
  1. Xiaolin Li

    1. University of Florida, Gainesville, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Judy Qiu

    1. Indiana University, Bloomington, USA

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    You can also search for this editor in PubMed   Google Scholar

  • One of the first books on cloud computing for eScience and data-intensive applications

  • Includes a special focus on programming models in clouds

  • Provides detailed case studies

  • Includes supplementary material: sn.pub/extras

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-viii
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  2. Systems and Applications

    1. Front Matter

      Pages 1-1
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    2. Scalable Deployment of a LIGO Physics Application on Public Clouds: Workflow Engine and Resource Provisioning Techniques

      • Suraj Pandey, Letizia Sammut, Rodrigo N. Calheiros, Andrew Melatos, Rajkumar Buyya
      Pages 3-25

    3. The FutureGrid Testbed for Big Data

      • Gregor von Laszewski, Geoffrey C. Fox
      Pages 27-59

    4. Cloud Networking to Support Data Intensive Applications

      • Maurício Tsugawa, Andréa Matsunaga, José A. B. Fortes
      Pages 61-81

    5. IaaS Cloud Benchmarking: Approaches, Challenges, and Experience

      • Alexandru Iosup, Radu Prodan, Dick Epema
      Pages 83-104

    6. GPU-Accelerated Cloud Computing for Data-Intensive Applications

      • Baoxue Zhao, Jianlong Zhong, Bingsheng He, Qiong Luo, Wenbin Fang, Naga K. Govindaraju
      Pages 105-129

    7. Adaptive Workload Partitioning and Allocation for Data Intensive Scientific Applications

      • Xin Yang, Xiaolin Li
      Pages 131-148

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

      • Jun Wang, Pengju Shang, Jiangling Yin
      Pages 149-174

  3. Resource Management

    1. Front Matter

      Pages 175-175
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    2. Efficient Task-Resource Matchmaking Using Self-adaptive Combinatorial Auction

      • Han Zhao, Xiaolin Li
      Pages 177-200

    3. Federating Advanced Cyberinfrastructures with Autonomic Capabilities

      • Javier Diaz-Montes, Ivan Rodero, Mengsong Zou, Manish Parashar
      Pages 201-227

  4. Programming Models

    1. Front Matter

      Pages 229-229
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    2. Migrating Scientific Workflow Management Systems from the Grid to the Cloud

      • Yong Zhao, Youfu Li, Ioan Raicu, Cui Lin, Wenhong Tian, Ruini Xue
      Pages 231-256

    3. Executing Storm Surge Ensembles on PAAS Cloud

      • Abhirup Chakraborty, Milinda Pathirage, Isuru Suriarachchi, Kavitha Chandrasekar, Craig Mattocks, Beth Plale
      Pages 257-276

    4. Cross-Phase Optimization in MapReduce

      • Benjamin Heintz, Abhishek Chandra, Jon Weissman
      Pages 277-302

    5. Asynchronous Computation Model for Large-Scale Iterative Computations

      • Yanfeng Zhang, Qixin Gao, Lixin Gao, Cuirong Wang
      Pages 303-328

  5. Cloud Storage

    1. Front Matter

      Pages 329-329
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    2. Big Data Storage and Processing on Azure Clouds: Experiments at Scale and Lessons Learned

      • Radu Tudoran, Alexandru Costan, Gabriel Antoniu, Brasche Goetz
      Pages 331-355

    3. Storage and Data Life Cycle Management in Cloud Environments with FRIEDA

      • Lavanya Ramakrishnan, Devarshi Ghoshal, Valerie Hendrix, Eugen Feller, Pradeep Mantha, Christine Morin
      Pages 357-378
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Keywords

About this book

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.

Editors and Affiliations

  • University of Florida, Gainesville, USA

    Xiaolin Li

  • Indiana University, Bloomington, USA

    Judy Qiu

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