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A highly practical approach toward achieving minimum data sets storage cost in the cloud

Yuan, Dong, Yang, Yun, Liu, Xiao, Li, Wenhao, Cui, Lizhen, Xu, Meng and Chen, Jinjun 2013, A highly practical approach toward achieving minimum data sets storage cost in the cloud, IEEE transactions on parallel and distributed systems, vol. 24, no. 6, pp. 1234-1244, doi: 10.1109/TPDS.2013.20.

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Title A highly practical approach toward achieving minimum data sets storage cost in the cloud
Author(s) Yuan, Dong
Yang, Yun
Liu, XiaoORCID iD for Liu, Xiao orcid.org/0000-0002-4151-8522
Li, Wenhao
Cui, Lizhen
Xu, Meng
Chen, Jinjun
Journal name IEEE transactions on parallel and distributed systems
Volume number 24
Issue number 6
Start page 1234
End page 1244
Total pages 11
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2013-06
ISSN 1045-9219
Keyword(s) Data sets storage
computation-storage tradeoff
computation- and data-intensive applications
cloud computing
Science & Technology
Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
WORKFLOW SYSTEMS
Summary Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application data sets can be stored in the cloud. Based on the pay-as-you-go model, storage strategies and benchmarking approaches have been developed for cost-effectively storing large volume of generated application data sets in the cloud. However, they are either insufficiently cost-effective for the storage or impractical to be used at runtime. In this paper, toward achieving the minimum cost benchmark, we propose a novel highly cost-effective and practical storage strategy that can automatically decide whether a generated data set should be stored or not at runtime in the cloud. The main focus of this strategy is the local-optimization for the tradeoff between computation and storage, while secondarily also taking users' (optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications with Amazon's cost model show that the cost-effectiveness of our strategy is close to or even the same as the minimum cost benchmark, and the efficiency is very high for practical runtime utilization in the cloud.
Language eng
DOI 10.1109/TPDS.2013.20
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30087722

Document type: Journal Article
Collection: School of Information Technology
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