Computation and Storage in the Cloud
Understanding the Trade-Offs
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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
Key Features
- 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
Readership
Researchers, practitioners, and graduate students in scientific computing seeking guidance for managing application datasets
Table of Contents
- Acknowledgements
- About the Authors
- Preface
- 1. Introduction
- 1.1 Scientific Applications in the Cloud
- 1.2 Key Issues of This Research
- 1.3 Overview of This Book
- 2. Literature Review
- 2.1 Data Management of Scientific Applications in Traditional Distributed Systems
- 2.2 Cost-Effectiveness of Scientific Applications in the Cloud
- 2.3 Data Provenance in Scientific Applications
- 2.4 Summary
- 3. Motivating Example and Research Issues
- 3.1 Motivating Example
- 3.2 Problem Analysis
- 3.3 Research Issues
- 3.4 Summary
- 4. Cost Model of Data Set Storage in the Cloud
- 4.1 Classification of Application Data in the Cloud
- 4.2 Data Provenance and DDG
- 4.3 Data Set Storage Cost Model in the Cloud
- 4.4 Summary
- 5. Minimum Cost Benchmarking Approaches
- 5.1 Static On-Demand Minimum Cost Benchmarking Approach
- 5.2 Dynamic On-the-Fly Minimum Cost Benchmarking Approach
- 5.3 Summary
- 6. Cost-Effective Data Set Storage Strategies
- 6.1 Data-Accessing Delay and Users’ Preferences in Storage Strategies
- 6.2 Cost-Rate-Based Storage Strategy
- 6.3 Local-Optimisation-Based Storage Strategy
- 6.4 Summary
- 7. Experiments and Evaluations
- 7.1 Experiment Environment
- 7.2 Evaluation of Minimum Cost Benchmarking Approaches
- 7.3 Evaluation of Cost-Effective Storage Strategies
- 7.4 Case Study of Pulsar Searching Application
- 7.5 Summary
- 8. Conclusions and Contributions
- 8.1 Summary of This Book
- 8.2 Key Contributions of This Book
- Appendix A. Notation Index
- Appendix B. Proofs of Theorems, Lemmas and Corollaries
- Appendix C. Method of Calculating λ Based on Users’ Extra Budget
- Bibliography
Product details
- No. of pages: 128
- Language: English
- Copyright: © Elsevier 2013
- Published: December 31, 2012
- Imprint: Elsevier
- Paperback ISBN: 9780124077676
- eBook ISBN: 9780124078796
About the Authors
Dong Yuan
Dong Yuan is currently a research fellow in School of Software and Electrical Engineering at Swinburne University of Technology, Melbourne, Australia. His research interests include data management in parallel and distributed systems, scheduling and resource management, grid and cloud computing.
Affiliations and Expertise
Swinburne University of Technology, Melbourne, Australia
Yun Yang
Yun Yang is currently a full professor in School of Software and Electrical Engineering at Swinburne University of Technology, Melbourne, Australia. Prior to joining Swinburne in 1999 as an associate professor, he was a lecturer and senior lecturer at Deakin University, Australia, during 1996-1999. He has coauthored four books and published over 200 papers in journals and refereed conference proceedings. He is currently on the Editorial Board of IEEE Transactions on Cloud Computing. His current research interests include software technologies, cloud computing, p2p/grid/cloud workflow systems, and service-oriented computing.
Affiliations and Expertise
Swinburne University of Technology, Melbourne, Australia
Jinjun Chen
Jinjun Chen received his PhD degree in Computer Science and Software Engineering from Swinburne University of Technology, Melbourne, Australia in 2007. He is currently an Associate Professor in the Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia. His research interests include Scientific workflow management and applications, workflow management and applications in Web service or SOC environments, workflow management and applications in grid (service)/cloud computing environments, software verification and validation in workflow systems, QoS and resource scheduling in distributed computing systems such as cloud computing, service oriented computing, semantics and knowledge management, cloud computing.
Affiliations and Expertise
University of Technology, Sydney, Australia
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