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Computation and Storage in the Cloud

Book Description

Computation and Storage in the Cloud is the first comprehensive and systematic work investigating the issue of computation and storage trade-off in the cloud in order to reduce the overall application cost. Scientific applications are usually computation and data intensive, where complex computation tasks take a long time for execution and the generated datasets are often terabytes or petabytes in size. Storing valuable generated application datasets can save their regeneration cost when they are reused, not to mention the waiting time caused by regeneration. However, the large size of the scientific datasets is a big challenge for their storage. By proposing innovative concepts, theorems and algorithms, this book will help bring the cost down dramatically for both cloud users and service providers to run computation and data intensive scientific applications in the cloud.

  • Covers cost models and benchmarking that explain the necessary tradeoffs for both cloud providers and users
  • Describes several novel strategies for storing application datasets in the cloud
  • Includes real-world case studies of scientific research applications
    • Covers cost models and benchmarking that explain the necessary tradeoffs for both cloud providers and users

    • Describes several novel strategies for storing application datasets in the cloud

  • Includes real-world case studies of scientific research applications

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Acknowledgements
  6. About the Authors
  7. Preface
  8. 1. Introduction
    1. 1.1 Scientific Applications in the Cloud
    2. 1.2 Key Issues of This Research
    3. 1.3 Overview of This Book
  9. 2. Literature Review
    1. 2.1 Data Management of Scientific Applications in Traditional Distributed Systems
    2. 2.2 Cost-Effectiveness of Scientific Applications in the Cloud
    3. 2.3 Data Provenance in Scientific Applications
    4. 2.4 Summary
  10. 3. Motivating Example and Research Issues
    1. 3.1 Motivating Example
    2. 3.2 Problem Analysis
    3. 3.3 Research Issues
    4. 3.4 Summary
  11. 4. Cost Model of Data Set Storage in the Cloud
    1. 4.1 Classification of Application Data in the Cloud
    2. 4.2 Data Provenance and DDG
    3. 4.3 Data Set Storage Cost Model in the Cloud
    4. 4.4 Summary
  12. 5. Minimum Cost Benchmarking Approaches
    1. 5.1 Static On-Demand Minimum Cost Benchmarking Approach
    2. 5.2 Dynamic On-the-Fly Minimum Cost Benchmarking Approach
    3. 5.3 Summary
  13. 6. Cost-Effective Data Set Storage Strategies
    1. 6.1 Data-Accessing Delay and Users’ Preferences in Storage Strategies
    2. 6.2 Cost-Rate-Based Storage Strategy
    3. 6.3 Local-Optimisation-Based Storage Strategy
    4. 6.4 Summary
  14. 7. Experiments and Evaluations
    1. 7.1 Experiment Environment
    2. 7.2 Evaluation of Minimum Cost Benchmarking Approaches
    3. 7.3 Evaluation of Cost-Effective Storage Strategies
    4. 7.4 Case Study of Pulsar Searching Application
    5. 7.5 Summary
  15. 8. Conclusions and Contributions
    1. 8.1 Summary of This Book
    2. 8.2 Key Contributions of This Book
  16. Appendix A. Notation Index
  17. Appendix B. Proofs of Theorems, Lemmas and Corollaries
  18. Appendix C. Method of Calculating λ Based on Users’ Extra Budget
  19. Bibliography