  The heterogeneous edge-cloud computing paradigm can provide a more optimal
direction to deploy scientific workflows than traditional distributed computing
or cloud computing environments. Due to the different sizes of scientific
datasets and some of these datasets must keep private, it is still a difficult
problem to finding an data placement strategy that can minimize data
transmission as well as placement cost. To address this issue, this paper
combines advantages of both edge and cloud computing to construct a data
placement model, which can balance data transfer time and data placement cost
using intelligent computation. The most difficult research challenge the model
solved is to consider many constrain in this hybrid computing environments,
which including shared datasets within individual and among multiple workflows
across various geographical regions. According to the constructed model, the
study propose a new data placement strategy named DE-DPSO-DPS, which using a
discrete particle swarm optimization algorithm with differential evolution
(DE-DPSO-DPA) to distribute these scientific datasets. The strategy also not
only consider the characteristics such as the number and storage capacity of
edge micro-datacenters, the bandwidth between different datacenters and the
proportion of private datasets, but also analysis the performance of algorithm
during the workflows execution. Comprehensive experiments are designed in
simulated heterogeneous edge-cloud computing environments demonstrate that the
data placement strategy can effectively reduce the data transmission time and
placement cost as compared to traditional strategies for data-sharing
scientific workflows.
