Cloud Computing for Data-Intensive Applications

  • Xiaolin Li
  • Judy Qiu

Table of contents

  1. Front Matter
    Pages i-viii
  2. Systems and Applications

    1. Front Matter
      Pages 1-1
    2. Suraj Pandey, Letizia Sammut, Rodrigo N. Calheiros, Andrew Melatos, Rajkumar Buyya
      Pages 3-25
    3. Gregor von Laszewski, Geoffrey C. Fox
      Pages 27-59
    4. Maurício Tsugawa, Andréa Matsunaga, José A. B. Fortes
      Pages 61-81
    5. Alexandru Iosup, Radu Prodan, Dick Epema
      Pages 83-104
    6. Baoxue Zhao, Jianlong Zhong, Bingsheng He, Qiong Luo, Wenbin Fang, Naga K. Govindaraju
      Pages 105-129
  3. Resource Management

    1. Front Matter
      Pages 175-175
    2. Javier Diaz-Montes, Ivan Rodero, Mengsong Zou, Manish Parashar
      Pages 201-227
  4. Programming Models

    1. Front Matter
      Pages 229-229
    2. Yong Zhao, Youfu Li, Ioan Raicu, Cui Lin, Wenhong Tian, Ruini Xue
      Pages 231-256
    3. Abhirup Chakraborty, Milinda Pathirage, Isuru Suriarachchi, Kavitha Chandrasekar, Craig Mattocks, Beth Plale
      Pages 257-276
    4. Benjamin Heintz, Abhishek Chandra, Jon Weissman
      Pages 277-302
    5. Yanfeng Zhang, Qixin Gao, Lixin Gao, Cuirong Wang
      Pages 303-328
  5. Cloud Storage

    1. Front Matter
      Pages 329-329
    2. Radu Tudoran, Alexandru Costan, Gabriel Antoniu, Brasche Goetz
      Pages 331-355
    3. Lavanya Ramakrishnan, Devarshi Ghoshal, Valerie Hendrix, Eugen Feller, Pradeep Mantha, Christine Morin
      Pages 357-378

About this book

Introduction

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.

Keywords

Benchmarking Big Data Cloud Computing Cloud Networking Cloud Services Cloud Storage Cyberinfrastructure Data Storage Data-intensive Computing Large-scale Computing Optimization Programming Models Social Media Workload Partitioning eScience

Editors and affiliations

  • Xiaolin Li
    • 1
  • Judy Qiu
    • 2
  1. 1.University of FloridaGainesvilleUSA
  2. 2.Indiana UniversityBloomingtonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-1905-5
  • Copyright Information Springer Science+Business Media New York 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4939-1904-8
  • Online ISBN 978-1-4939-1905-5
  • About this book