Computation and storage in the cloud understanding the trade-offs
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 computatio...
| Autor principal: | |
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Elsevier
2013.
Waltham, MA : 2013. |
| Edición: | 1st ed |
| Colección: | Elsevier insights.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628584206719 |
Tabla de Contenidos:
- Front Cover; Computation and Storage in the Cloud; Copyright Page; 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; 2.1.1 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 Cloud2.2.2 Trade-Off Between Computation and Storage 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; 3.2.1 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 Data Set Storage in the Cloud; 3.3.2 Minimum Cost Benchmarking Approaches; 3.3.3 Cost-Effective Storage Strategies
- 3.4 Summary4 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; 5.1.1 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 DDG5.1.3.1 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-Dimensional PSS of DDG Segment with Two Branches
- 5.2.3.2 High-Dimensional PSS of a General DDG5.2.4 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; 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; 6.2.1 Algorithms for the Strategy; 6.2.1.1 Algorithm for Deciding Newly Generated Data Sets' Storage Status
- 6.2.1.2 Algorithm for Deciding Stored Data Sets' Storage Status Due to Usage Frequencies Change
