云计算环境下低成本存储科学数据的演化CTT-SP算法

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

云计算系统强大的计算能力和存储容量,使得科学家可以在其上部署计算型和数据密集型的应用,并把大量的应用数据存储在云计算环境下。基于云服务即用即付模型,针对原有数据存储状态,考虑云服务价格变化所产生的状态调整成本,同时为降低存储大量生成的科学数据的成本,在传统最小成本基准的CTT-SP算法的基础上,提出了一种演化CTT-SP算法。在云计算环境下针对云服务的新价格,该算法可自动决定所生成的科学数据是否需要存储,从而使计算和存储达到更佳的平衡。以亚马逊的成本模型为例,对大量随机数据集进行实验,结果表明,当云服务价格变化后,所提演化CTT-SP算法有效地降低了存储科学数据的总成本。

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 minimum cost benchmarking CTT-SP algorithm for cost-eHectively 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.