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Most large‐scale scientific workflows take place in multiple collaborative datacenters for access to community‐wide resources, while adhering to each datacenter's non‐uniform resource limits. However, moving both initial input datasets with predetermined locations and intermediate datasets needing placement decisions across geo‐distributed datacenters hinders efficient execution of large‐scale data‐intensive...
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...
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