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The heterogeneous edge-cloud computing paradigm can provide a more optimaldirection to deploy scientific workflows than traditional distributed computingor cloud computing environments. Due to the different sizes of scientificdatasets and some of these datasets must keep private, it is still a difficultproblem to finding an data placement strategy that can minimize datatransmission as well as placement cost. To address this issue, this papercombines advantages of both edge and cloud computing to construct a dataplacement model, which can balance data transfer time and data placement costusing intelligent computation. The most difficult research challenge the modelsolved is to consider many constrain in this hybrid computing environments,which including shared datasets within individual and among multiple workflowsacross various geographical regions. According to the constructed model, thestudy propose a new data placement strategy named DE-DPSO-DPS, which using adiscrete particle swarm optimization algorithm with differential evolution(DE-DPSO-DPA) to distribute these scientific datasets. The strategy also notonly consider the characteristics such as the number and storage capacity ofedge micro-datacenters, the bandwidth between different datacenters and theproportion of private datasets, but also analysis the performance of algorithmduring the workflows execution. Comprehensive experiments are designed insimulated heterogeneous edge-cloud computing environments demonstrate that thedata placement strategy can effectively reduce the data transmission time andplacement cost as compared to traditional strategies for data-sharingscientific workflows.

Optimal Data Placement for Data-Sharing Scientific Workflows in Heterogeneous Edge-Cloud Computing Environments