Ceph CRUSH Data Distribution Algorithms

Xiao Yang Liang; Zhang Cen Guan

doi:10.4028/www.scientific.net/AMM.596.196

Paper Titles

Rough Match Function Based on Topological Structure
p.179

RETRACTED: The Effect of Empathic Technology on Artificial Intelligence
p.183

Research of Smart Home System Based on Handheld Device
p.188

A Self-Adaptive Harmony Search Algorithm for Unconstrained Optimization Problems
p.192

Ceph CRUSH Data Distribution Algorithms
p.196

Forecasting Consumer Confidence Index Based on Radial Basis Function Neural Network
p.200

Performance Comparison of Energy-Aware Task Scheduling with GA and CRO Algorithms in Cloud Environment
p.204

Rapidly Analyze the Fault Tree with Numerical Methods
p.209

Research of Differential Evolution Algorithm for Motor Control
p.216

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 596Ceph CRUSH Data Distribution Algorithms

Ceph CRUSH Data Distribution Algorithms

Abstract:

CRUSH is one of the Ceph module,mainly solve the controllable, extensible, decentralized distribution of data copy. Ceph's biggest characteristics is a distributed metadata server is through CRUSH algorithm to allocate file storage location, its core is RADOS (Reliable, Autonomic Distributed Object Store), an object storage cluster, provide high availability of the object itself, error detection and repair.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 596)

Pages:

196-199

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.596.196

Citation:

Cite this paper

Online since:

July 2014

Authors:

Xiao Yang Liang*, Zhang Cen Guan

Keywords:

Bucket, Cloud Storage, Crush

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] M Armbrust, A Fox, R Griffith, et al. A view of cloud computing[J]. Communications of the ACM, 2010, 53(4): 50-58.

Google Scholar

[2] I Foster, Y Zhao, I Raicu, et al. Cloud computing and grid computing 360-degree compared[C]. Grid Computing Environments Workshop, 2008. GCE'08. Ieee, 2008: 1-10.

DOI: 10.1109/gce.2008.4738445

Google Scholar

[3] L Dequan, W Ying, X Anyuan, et al. High Performance Computing Model for Processing Meteorological Data in Cluster System[J]. Journal of Convergence Information Technology, 2011, 6.

Google Scholar

[4] J Dean, S Ghemawat. MapReduce: simplified data processing on large clusters[J]. Communications of the ACM, 2008, 51(1): 107-113.

DOI: 10.1145/1327452.1327492

Google Scholar

[5] D Howe, M Costanzo, P Fey, et al. Big data: The future of biocuration[J]. Nature, 2008, 455(7209): 47-50.

Google Scholar

[6] J Han, M Kamber. Data mining: concepts and techniques[M]. Morgan Kaufmann, (2006).

Google Scholar

[7] F Chang, J Dean, S Ghemawat, et al. Bigtable: A distributed storage system for structured data[J]. ACM Transactions on Computer Systems (TOCS), 2008, 26(2): 4.

DOI: 10.1145/1365815.1365816

Google Scholar