The Community for Technology Leaders
2017 Fifth International Conference on Advanced Cloud and Big Data (CBD) (2017)
Shanghai, China
Aug. 13, 2017 to Aug. 16, 2017
ISBN: 978-1-5386-1072-5
pp: 63-68
ABSTRACT
Scientific workflow usually needs to be performed in multiple collaborative datacenters for the requirement of accessing community-wide resources. However, the movements of initial input data and intermediate data across geo-distributed datacenters would hinder efficient execution of large-scale dataintensive scientific workflows. In this paper, a novel scheduling approach based on graph partition is proposed for the execution of data-intensive scientific workflow in geo-distributed datacenters, aiming at the optimization of the overall data transfer cost. Simulations show that our algorithm significantly reduces the overall geo-distributed data transfer and demonstrate its effectiveness.
INDEX TERMS
Scheduling, Partitioning algorithms, Processor scheduling, Linear programming, Data transfer, Data models
CITATION

J. Chen, J. Zhang and A. Song, "Efficient Data and Task Co-Scheduling for Scientific Workflow in Geo-Distributed Datacenters," 2017 Fifth International Conference on Advanced Cloud and Big Data (CBD), Shanghai, China, 2017, pp. 63-68.
doi:10.1109/CBD.2017.19
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