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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/196660
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- A local-optimisation based strategy for cost-effective datasets storage of scientific applications in the cloud
- Yuan, Dong; Yang, Yun; Liu, Xiao; Chen, Jinjun
- 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 datasets can be stored in the cloud. However, due to the pay-as-you-go model, the datasets should be strategically stored in order to reduce the overall application cost. In this paper, by utilising Data Dependency Graph (DDG) from data provenances in scientific applications, deleted datasets can be regenerated, and as such we develop a novel cost-effective datasets storage strategy that can automatically store appropriate datasets in the cloud. This strategy achieves a localised optimal trade-off between computation and storage, meanwhile also taking users' tolerance of data accessing delay into consideration. Simulations conducted on general (random) datasets and a specific astrophysics pulsar searching application with Amazon's cost model show that our strategy can reduce the application cost significantly.
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- Conference paper
- Research centre
- Swinburne University of Technology. Faculty of Information and Communication Technologies
- Proceedings of the 4th IEEE International Conference on Cloud Computing (CLOUD 2011), Washington, District of Columbia, United States, 04-09 July 2011 / Ling Liu and Manish Parashar (eds.), pp. 179-186
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