2017 Fifth International Conference on Advanced Cloud and Big Data (CBD) (2017)
Aug. 13, 2017 to Aug. 16, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CBD.2017.19
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.
Scheduling, Partitioning algorithms, Processor scheduling, Linear programming, Data transfer, Data models
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.