Abstract
Cloud computing emerges with high performance computing, massive data storage and easy access of the Internet. By deploying cloud computing, scientific workflows can be more cost-effective. During workflow execution, large volume data transmission may occur among multiple datacenters, hence incur large cost. Traditional approaches aim at finding data placement strategies for individual workflows only to reduce data transmission cost. However, workflows may share some datasets. Therefore, optimal data placement considering individual workflows in isolation is not necessarily optimal for the situation of multiple workflows as a whole. In this paper, by facilitation of Particle Swarm Optimization (PSO), we build a novel data transmission cost model for developing a new multi-datacenter cost-effective data placement strategy. The experimental results show that our strategy is much better than its traditional counterparts.
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© 2014 Springer-Verlag Berlin Heidelberg
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Li, X. et al. (2014). A New Particle Swarm Optimization-Based Strategy for Cost-Effective Data Placement in Scientific Cloud Workflows. In: Park, J., Pan, Y., Kim, CS., Yang, Y. (eds) Future Information Technology. Lecture Notes in Electrical Engineering, vol 309. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55038-6_18
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DOI: https://doi.org/10.1007/978-3-642-55038-6_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-55037-9
Online ISBN: 978-3-642-55038-6
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