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A data placement strategy in scientific cloud workflows
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DOI：10.1016/j.future.2010.02.004.png)
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In scientific cloud workflows, large amounts of application data need to be stored in distributed data centres. To effectively store these data, a data manager must intelligently select data centres in which these data will reside. This is, however, not the case for data which must have a fixed location. When one task needs several datasets located in different data centres, the movement of large volumes of data becomes a challenge. In this paper, we propose a matrix based k-means clustering strategy for data placement in scientific cloud workflows. The strategy contains two algorithms that group the existing datasets in k data centres during the workflow build-time stage, and dynamically clusters newly generated datasets to the most appropriate data centres - based on dependencies - during the runtime stage. Simulations show that our algorithm can effectively reduce data movement during the workflow's execution. (c) 2010 Elsevier B.V. All rights reserved.
Keyword:
Data management
Scientific workflow
Cloud computing
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