In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific\nworkflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the\ndynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient\ndata placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic\ncloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we\npropose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators\n(DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the\nscientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation\nresults show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid\ncloud environments.
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