  The heterogeneous edge-cloud computing paradigm can provide an optimal
solution to deploy scientific workflows compared to cloud computing or other
traditional distributed computing environments. Owing to the different sizes of
scientific datasets and the privacy issue concerning some of these datasets, it
is essential to find a data placement strategy that can minimize data
transmission time. Some state-of-the-art data placement strategies combine edge
computing and cloud computing to distribute scientific datasets. However, the
dynamic distribution of newly generated datasets to appropriate datacenters and
exiting the spent datasets are still a challenge during workflows execution. To
address this challenge, this study not only constructs a data placement model
that includes shared datasets within individual and among multiple workflows
across various geographical regions, but also proposes a data placement
strategy (DYM-RL-DPS) based on algorithms of two stages. First, during the
build-time stage of workflows, we use the discrete particle swarm optimization
algorithm with differential evolution to pre-allocate initial datasets to
proper datacenters. Then, we reformulate the dynamic datasets distribution
problem as a Markov decision process and provide a reinforcement learning-based
approach to learn the optimal strategy in the runtime stage of scientific
workflows. Through simulating heterogeneous edge-cloud computing environments,
we designed comprehensive experiments to demonstrate the superiority of
DYM-RL-DPS. The results of our strategy can effectively reduce the data
transmission time as compared to other strategies.
