Abstract
In the cloud, data replication strategies have been adopted by many geographical data centers in order to improve data availability, decrease the access latency and reduce the data communication cost. In this paper, we propose a novel data replication strategy to reduce the cost of data storage and transfer for workflow applications. In our approach, we partition data storage space into two categories, and classify dataset types into three categories. We also develop a data replication algorithm in the buildtime stage with different determinant levels of data dependency, access frequency, storage capacities of data centers, and size of datasets. The case study shows that our approach can significantly decrease the total cost of data storage and transfer for workflow applications.
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