Computation and storage in the cloud : understanding the trade-offs / Dong Yuan and Yun Yang, Centre for Computing and Engineering Software Systems, Faculty of Information and Communication Technologies, Swinburne University of Technology, Hawthorn, Melbourne, Australia, Jinjun Chen, Centre for Innovation in IT Services and Applications, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia.

Format:

Book

Author/Creator:

Yuan, Dong.

Yang, Yun, author.

Chen, Jinjun, author.

Series:

Elsevier insights.

Elsevier insights Computation and storage in the cloud

Language:

English

Subjects (All):

Cloud computing.

Virtual storage (Computer science).

Physical Description:

1 online resource (xiv, 113 pages) : illustrations (some color).

Edition:

1st ed.

Place of Publication:

Waltham, MA : Elsevier, 2013.

Language Note:

English

System Details:

text file

Summary:

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

Contents:

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

Notes:

Description based upon print version of record.

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

ISBN:

9781283941570

1283941570

9780124078796

0124078796

OCLC:

826857706