Computer Science ›› 2017, Vol. 44 ›› Issue (12): 163-168.doi: 10.11896/j.issn.1002-137X.2017.12.031

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Evolutionary CTT-SP Algorithm for Cost-effectively Storing Scientific Datasets in Cloud

GUO Mei, YUAN Dong and YANG Yun   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications in the cloud,where large application datasets can be stored.Based on the cloud service’s pay-as-you-go model,taking the status adjustment cost caused by cloud service’s price changes into consideration for the original datasets storage status,we proposed an evolutionary CTT-SP algorithm based on the traditional mini-mum cost benchmarking CTT-SP algorithm for cost-effectively storing large volume of generated scientific datasets in the cloud.The algorithm can automatically decide whether a generated dataset should be stored or not in the cloud,and also achieve better trade-off between computation and storage at the new price.Random simulations conducted with Amazon’s cost model show that the proposed evolutionary CTT-SP algorithm can save the overall cost of storing scientific datasets significantly when the cloud service’s price changes.

Key words: Datasets storage,Computation-storage trade-off,Cloud computing,Scientific application,Cost

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