Graph-cut based coscheduling strategy towards efficient execution of scientific workflows in collaborative cloud environments

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Conference Proceeding
Proceedings - 2011 12th IEEE/ACM International Conference on Grid Computing, Grid 2011, 2011, pp. 34 - 41
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Recently, cloud computing has emerged as a promising computing infrastructure for performing scientific workflows by providing on-demand resources. Meanwhile, it is convenient for scientific collaboration since different cloud environments used by the researchers are connected through Internet. However, the significant latency arising from frequent access to large datasets and the corresponding data movements across geo-distributed data centers has been an obstacle to hinder the efficient execution of data-intensive scientific workflows. In this paper, we propose a novel graph-cut based data and task co scheduling strategy for minimizing the data transfer across geo-distributed data centers. Specifically, a dependency graph is firstly constructed from workflow provenance and cut into sub graphs according to the datasets which must appear in fixed data centers by a multiway cut algorithm
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