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Compared to traditional distributed computing environments such as grids,cloud computing provides a more cost-effective way to deploy scientificworkflows. Each task of a scientific workflow requires several large datasetsthat are located in different datacenters from the cloud computing environment,resulting in serious data transmission delays. Edge computing reduces the datatransmission delays and supports the fixed storing manner for scientificworkflow private datasets, but there is a bottleneck in its storage capacity.It is a challenge to combine the advantages of both edge computing and cloudcomputing to rationalize the data placement of scientific workflow, andoptimize the data transmission time across different datacenters. Traditionaldata placement strategies maintain load balancing with a given number ofdatacenters, which results in a large data transmission time. In this study, aself-adaptive discrete particle swarm optimization algorithm with geneticalgorithm operators (GA-DPSO) was proposed to optimize the data transmissiontime when placing data for a scientific workflow. This approach considered thecharacteristics of data placement combining edge computing and cloud computing.In addition, it considered the impact factors impacting transmission delay,such as the band-width between datacenters, the number of edge datacenters, andthe storage capacity of edge datacenters. The crossover operator and mutationoperator of the genetic algorithm were adopted to avoid the prematureconvergence of the traditional particle swarm optimization algorithm, whichenhanced the diversity of population evolution and effectively reduced the datatransmission time. The experimental results show that the data placementstrategy based on GA-DPSO can effectively reduce the data transmission timeduring workflow execution combining edge computing and cloud computing.

A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing