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Computation and Storage in the Cloud
 
 

Computation and Storage in the Cloud, 1st Edition

Understanding the Trade-Offs

 
Computation and Storage in the Cloud, 1st Edition,Dong Yuan,Yun Yang,Jinjun Chen,ISBN9780124077676
 
 
 

  &      &      

Elsevier

9780124077676

9780124078796

128

229 X 152

Innovative strategies and benchmarks for dataset storage of scientific applications

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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

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

Readership

Researchers, practitioners, and graduate students in scientific computing seeking guidance for managing application datasets.

Dong Yuan

Dong Yuan is a postdoctoral research fellow in the Centre for Computing and Engineering Software Systems, Swinburne University of Technology. His research interests include: Parallel and Distributed Computing; Cloud and Grid Computing; Data Management; Workflow Technology; Scientific Applications and E-Science; Service Computing and BPM.

Affiliations and Expertise

Swinburne University of Technology, Melbourne, Australia

Yun Yang

Yun Yang received a Master of Engineering degree from The University of Science and Technology of China, Hefei, China, in 1987, and a PhD degree from The University of Queensland, Brisbane, Australia, in 1992, all in computer science. He is currently a full Professor in the Faculty of Information and Communication Technologies at Swinburne University of Technology, Melbourne, Australia. Prior to joining Swinburne as an Associate Professor in late 1999, he was a Lecturer and Senior Lecturer at Deakin University during 1996-1999. Before that, he was a Research Scientist at DSTC - Cooperative Research Centre for Distributed Systems Technology during 1993-1996. He also worked at Beihang University in China during 1987-1988. He has published about 200 papers on journals and refereed conferences. His research interests include software engineering; p2p, grid and cloud computing; workflow systems; service-oriented computing; Internet computing applications; and CSCW.

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

Computation and Storage in the Cloud, 1st Edition

CHAPTER 1  INTRODUCTION 1
1.1    Scientific Applications in the Cloud
1.2    Key Issues of this Research
1.3    Overview of this Book
CHAPTER 2 LITERATURE REVIEW
2.1    Data Management of Scientific Applications in Traditional Distributed Systems
2.1.1    Data Management in Grid
2.1.2    Data Management in Grid Workflows
2.1.3   Data Management in Other Distributed Systems
2.2    Cost-Effectiveness of Scientific Applications in the Cloud
2.2.1    Cost-Effectiveness of Deploying Scientific Applications in the Cloud
2.2.2    Trade-Off between Computation and Storage in the Cloud
2.3    Data Provenance in Scientific Applications
2.4    Summary 16
CHAPTER 3 MOTIVATING EXAMPLE AND RESEARCH ISSUES
3.1    Motivating Example
3.2    Problem Analysis
3.2.1    Requirements and Challenges of Deploying Scientific Applications in the Cloud
3.2.2    Bandwidth Cost of Deploying Scientific Applications in the Cloud
3.3    Research Issues
3.3.1    Cost Model for Datasets Storage in the Cloud
3.3.2    Minimum Cost Benchmarking Approaches
3.3.3    Cost-Effective Storage Strategies
3.4    Summary
CHAPTER 4 COST MODEL OF DATASETS STORAGE IN THE CLOUD
4.1    Classification of Application Data in the Cloud
4.2    Data Provenance and Data Dependency Graph (DDG)
4.3    Datasets Storage Cost Model in the Cloud
4.4    Summary
CHAPTER 5  MINIMUM COST BENCHMARKING APPROACHES
5.1    Static On-Demand Minimum Cost Benchmarking Approach
5.1.1    CTT-SP Algorithm for Linear DDG
5.1.2    Minimum Cost Benchmarking Algorithm for DDG with One Block
5.1.2.1    Constructing CTT for DDG with one block
5.1.2.2    Setting weights to different types of edges
5.1.2.3    Steps of finding MCSS for DDG with one sub-branch in one block
5.1.3    Minimum Cost Benchmarking Algorithm for General DDG
5.1.3.1    General CTT-SP algorithm for different situations
5.1.3.2    Pseudo-code of general CTT-SP algorithm
5.2    Dynamic on-the-fly Minimum Cost Benchmarking Approach
5.2.1    PSS for a DDG_LS
5.2.1.1    Different MCSSs of a DDG_LS in a solution space
5.2.1.2    Range of MCSSs’ cost rates for a DDG_LS
5.2.1.3    Distribution of MCSSs in the PSS of a DDG_LS
5.2.2    Algorithms for Calculating PSS of a DDG_LS
5.2.3    PSS for a General DDG (or DDG Segment)
5.2.3.1    Three dimension PSS of DDG segment with two branches
5.2.3.2    High dimension PSS of a general DDG
5.2.4    Dynamic on-the-fly Minimum Cost Benchmarking
5.2.4.1    Minimum cost benchmarking by merging and saving PSSs in a hierarchy
5.2.4.2    Updating of the minimum cost benchmark on the fly
5.3    Summary
CHAPTER 6  COST-EFFECTIVE DATASETS STORAGE STRATEGIES
6.1    Data Accessing Delay and Users’ Preferences in Storage Strategies
6.2    Cost Rate Based Storage Strategy
6.2.1    Algorithms for the Strategy
6.2.1.1    Algorithm for deciding newly generated datasets’ storage status
6.2.1.2    Algorithm for deciding stored datasets’ storage status due to usage frequencies change
6.2.1.3    Algorithm for deciding regenerated datasets’ storage status
6.2.2    Cost-Effectiveness Analysis
6.3    Local-Optimisation Based Storage Strategy
6.3.1    Algorithms and Rules for the Strategy
6.3.1.1    Enhanced CTT-SP algorithm for linear DDG
6.3.1.2    Rules in the Strategy
6.3.2    Cost-Effectiveness Analysis
6.4    Summary
CHAPTER 7  EXPERIMENTS AND EVALUATIONS
7.1    Experiment Environment
7.2    Evaluation of Minimum Cost Benchmarking Approaches
7.2.1    Cost-Effectiveness Evaluation of the Minimum Cost Benchmark
7.2.2    Efficiency Evaluation of Two Benchmarking Approaches
7.3    Evaluation of Cost-Effective Storage Strategies
7.3.1    Cost-Effectiveness of Two Storage Strategies
7.3.2    Efficiency Evaluation of Two Storage Strategies
7.4    Case Study of Pulsar Searching Application
7.4.1    Utilisation of Minimum Cost Benchmarking Approaches
7.4.2    Utilisation of Cost-Effective Storage Strategies
7.5    Summary
CHAPTER 8 CONCLUSIONS AND FUTURE WORK
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 ? BASED ON USERS’ EXTRA BUDGET
BIBLIOGRAPHY

Quotes and reviews

"Cloud computing systems charge for both data storage and for calculating, say Yuan, Yang…and Chen…, so there is a trade-off between storing large data sets in the cloud or deleting them and regenerating then each time they are needed. They suggest some approaches to figuring out which is cheaper… they cover motivating example and research issues, a cost model of data set storage in the cloud, minimum cost benchmarking approaches,…"--ProtoView.com, January 2014
"Cloud computing systems charge for both data storage and for calculating, say Yuan, Yang….and Chen…so there is a trade-off between storing large data sets in the cloud or deleting them and regenerating then each time they are needed. They suggest some approaches to figuring out which is cheaper."--Reference & Research Book News, December 2013
"…this book does a good job at tackling a variety of complex subjects. It brings forward state-of-the-art concepts and elaborate algorithms, illustrates issues related to cost-effectiveness, and helps both cloud providers and users get a grip on the intricate world of cloud computing."--Help Net Security online, August 28, 2013

 
 
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