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Computation and Storage in the Cloud: Understanding the Trade-Offs [Mīkstie vāki]

(Swinburne University of Technology, Melbourne, Australia), (Swinburne University of Technology, Melbourne, Australia), (University of Technology, Sydney, Australia)
  • Formāts: Paperback / softback, 128 pages, height x width: 229x152 mm, weight: 180 g
  • Izdošanas datums: 01-Feb-2013
  • Izdevniecība: Elsevier Science Publishing Co Inc
  • ISBN-10: 0124077676
  • ISBN-13: 9780124077676
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  • Mīkstie vāki
  • Cena: 43,00 €
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Computation and Storage in the Cloud: Understanding the Trade-OffsPalielināt
  • Formāts: Paperback / softback, 128 pages, height x width: 229x152 mm, weight: 180 g
  • Izdošanas datums: 01-Feb-2013
  • Izdevniecība: Elsevier Science Publishing Co Inc
  • ISBN-10: 0124077676
  • ISBN-13: 9780124077676
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780124077676.html
Keywords:

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

Recenzijas

"Cloud computing systems charge for both data storage and for calculating, say Yuan, Yangand 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 Chenso 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 and 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

Papildus informācija

Innovative strategies and benchmarks for dataset storage of scientific applications
Acknowledgements ix
About the Authors xi
Preface xiii
1 Introduction
1(4)
1.1 Scientific Applications in the Cloud
1(2)
1.2 Key Issues of This Research
3(1)
1.3 Overview of This Book
3(2)
2 Literature Review
5(10)
2.1 Data Management of Scientific Applications in Traditional Distributed Systems
5(5)
2.1.1 Data Management in Grid
6(2)
2.1.2 Data Management in Grid Workflows
8(1)
2.1.3 Data Management in Other Distributed Systems
9(1)
2.2 Cost-Effectiveness of Scientific Applications in the Cloud
10(2)
2.2.1 Cost-Effectiveness of Deploying Scientific Applications in the Cloud
10(1)
2.2.2 Trade-Off Between Computation and Storage in the Cloud
11(1)
2.3 Data Provenance in Scientific Applications
12(1)
2.4 Summary
12(3)
3 Motivating Example and Research Issues
15(8)
3.1 Motivating Example
15(2)
3.2 Problem Analysis
17(2)
3.2.1 Requirements and Challenges of Deploying Scientific Applications in the Cloud
17(1)
3.2.2 Bandwidth Cost of Deploying Scientific Applications in the Cloud
18(1)
3.3 Research Issues
19(2)
3.3.1 Cost Model for Data Set Storage in the Cloud
19(1)
3.3.2 Minimum Cost Benchmarking Approaches
20(1)
3.3.3 Cost-Effective Storage Strategies
20(1)
3.4 Summary
21(2)
4 Cost Model of Data Set Storage in the Cloud
23(6)
4.1 Classification of Application Data in the Cloud
23(1)
4.2 Data Provenance and DDG
23(2)
4.3 Data Set Storage Cost Model in the Cloud
25(2)
4.4 Summary
27(2)
5 Minimum Cost Benchmarking Approaches
29(36)
5.1 Static On-Demand Minimum Cost Benchmarking Approach
30(13)
5.1.1 CTT-SP Algorithm for Linear DDG
30(2)
5.1.2 Minimum Cost Benchmarking Algorithm for DDG with One Block
32(1)
5.1.2.1 Constructing CTT for DDG with One Block
33(1)
5.1.2.2 Setting Weights to Different Types of Edges
34(2)
5.1.2.3 Steps of Finding MCSS for DDG with One Sub-Branch in One Block
36(2)
5.1.3 Minimum Cost Benchmarking Algorithm for General DDG
38(1)
5.1.3.1 General CTT-SP Algorithm for Different Situations
38(1)
5.1.3.2 Pseudo-Code of General CTT-SP Algorithm
39(4)
5.2 Dynamic On-the-Fly Minimum Cost Benchmarking Approach
43(21)
5.2.1 PSS for a DDG_LS
44(1)
5.2.1.1 Different MCSSs of a DDG_LS in a Solution Space
44(1)
5.2.1.2 Range of MCSSs' Cost Rates for a DDG_LS
45(2)
5.2.1.3 Distribution of MCSSs in the PSS of a DDG_LS
47(3)
5.2.2 Algorithms for Calculating PSS of a DDG_LS
50(3)
5.2.3 PSS for a General DDG (or DDG Segment)
53(1)
5.2.3.1 Three-Dimensional PSS of DDG Segment with Two Branches
54(2)
5.2.3.2 High-Dimensional PSS of a General DDG
56(2)
5.2.4 Dynamic On-the-Fly Minimum Cost Benchmarking
58(1)
5.2.4.1 Minimum Cost Benchmarking by Merging and Saving PSSs in a Hierarchy
58(3)
5.2.4.2 Updating of the Minimum Cost Benchmark on the Fly
61(3)
5.3 Summary
64(1)
6 Cost-Effective Data Set Storage Strategies
65(10)
6.1 Data-Accessing Delay and Users' Preferences in Storage Strategies
65(1)
6.2 Cost-Rate-Based Storage Strategy
66(3)
6.2.1 Algorithms for the Strategy
67(1)
6.2.1.1 Algorithm for Deciding Newly Generated Data Sets' Storage Status
67(1)
6.2.1.2 Algorithm for Deciding Stored Data Sets' Storage Status Due to Usage Frequencies Change
68(1)
6.2.1.3 Algorithm for Deciding Regenerated Data Sets' Storage Status
68(1)
6.2.2 Cost-Effectiveness Analysis
69(1)
6.3 Local-Optimisation-Based Storage Strategy
69(5)
6.3.1 Algorithms and Rules for the Strategy
70(1)
6.3.1.1 Enhanced CTT-SP Algorithm for Linear DDG
70(2)
6.3.1.2 Rules in the Strategy
72(1)
6.3.2 Cost-Effectiveness Analysis
73(1)
6.4 Summary
74(1)
7 Experiments and Evaluations
75(16)
7.1 Experiment Environment
75(1)
7.2 Evaluation of Minimum Cost Benchmarking Approaches
75(7)
7.2.1 Cost-Effectiveness Evaluation of the Minimum Cost Benchmark
76(1)
7.2.2 Efficiency Evaluation of Two Benchmarking Approaches
77(5)
7.3 Evaluation of Cost-Effective Storage Strategies
82(4)
7.3.1 Cost-Effectiveness of Two Storage Strategies
82(2)
7.3.2 Efficiency Evaluation of Two Storage Strategies
84(2)
7.4 Case Study of Pulsar Searching Application
86(4)
7.4.1 Utilisation of Minimum Cost Benchmarking Approaches
86(1)
7.4.2 Utilisation of Cost-Effective Storage Strategies
87(3)
7.5 Summary
90(1)
8 Conclusions and Contributions
91(4)
8.1 Summary of This Book
91(1)
8.2 Key Contributions of This Book
92(3)
Appendix A Notation Index 95(2)
Appendix B Proofs of Theorems, Lemmas and Corollaries 97(10)
Appendix C Method of Calculating and X Based an Users' Extra Budget 107(2)
Bibliography 109
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. 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. 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.