Research of Cloud Storage and Data Read-Write Technology

Hong Jun Chen; Tao  Tan; Xue Qin Wu

doi:10.4028/www.scientific.net/AMM.347-350.3555

Paper Titles

Image Recognition Technology Based on Artificial Intelligence
p.3537

An Improved Edge Detection Algorithm Based on Canny Operator
p.3541

Deformation Analysis of Short and Medium Span Curved Beam Bridge
p.3546

The Research of Oil & Gas Energy Saving Index Prediction Based on Neural Network and Quantum-Behaved Particle Swarm Optimization
p.3550

Research of Cloud Storage and Data Read-Write Technology
p.3555

A New Method of SAR Airdrome Image Edge Detection Based on Image Character
p.3560

Researching of Chaotic Characteristics and Forecasting of Elman Network on the Communication Traffic
p.3565

An Accurate Collision Detection Method for Cable Simulation
p.3571

Application of Wavelet Transform in Image Processing in Aviation Engine Damage
p.3576

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350Research of Cloud Storage and Data Read-Write...

Research of Cloud Storage and Data Read-Write Technology

Abstract:

Cloud storage is a promising market. In this paper, cloud storage technology and cloud data read-write technology is researched. Cloud storage has a four-layer structure model. Cloud storage uses disaster recovery and backup technology to improve the availability and reliability of the system. The request of data read-write in cloud storage is a temporary URL, which is constructed according to the algorithm. Send read-write request through push sequentially, and write by transfer between the primary chunks with the copies. The efficiency of cloud storage depends on the number of serves and clients. In order to improve the efficiency of data reading and writing, there is proposed three envisaged.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

3555-3559

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.3555

Citation:

Cite this paper

Online since:

August 2013

Authors:

Hong Jun Chen, Tao Tan, Xue Qin Wu

Keywords:

Backup, Chunk, Cloud Data, Cloud Storage, Disaster Recovery, Master, Quest, Read, Temporary URL, Write

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Kenneth Cukier, Data Everywhere, Economist, (2010).

Google Scholar

[2] Cory Doctorow, Big data: Welcome to the petacentre, Nature, Vol. 455, No. 7209, (2008).

DOI: 10.1038/455016a

Google Scholar

[3] Qianchen, QianniChen Cloud Computing and Its Key Technology, Computer Application，Vol. 29, No. 9, (2009).

Google Scholar

[4] Yongwei Wu, Xiaomen Huang, Cloud Storage, China Computer Federation Communications, Vol. 5, No. 6, (2009).

Google Scholar

[5] Google File System(GFS), http: /wenku. baidu. com/view/8a839535ee06eff9aef8074d. html, (2012).

Google Scholar

[6] Amazon Simple Storage Service (S3), http: /aws. amazon. com/cn/s3/, (2013).

Google Scholar

[7] Cloud data deduplication, http: /www. freepatentsonline. com/8285681. html, (2012).

Google Scholar

[8] Adam Jacobs, The pathologies of big data, Communication of the ACM, 2009, vol. 52, pp.36-44.

Google Scholar

[9] Jeffrey Cohen, Brian Dolan, Mark Dunlap, Joseph M. Hellerstein, and Caleb Welton. MAD skills: new analysis practices for big data, PVLDB, vol. 52, , 2009, p.1481～1492.

DOI: 10.14778/1687553.1687576

Google Scholar

[10] Zhen Kong, Mechanism design for stochastic virtual resource allocation in non-coope rative cloud syst ems, Proceedings of IEEE International Conference on Cloud Computing, (2011).

DOI: 10.1109/cloud.2011.82

Google Scholar