This paper presents a method for using distributed computing technology in scientific workflows, which can achieve parallel execution of parameter sweep workflows on ad-hoc network computing resources, thereby significantly improving workflow execution performance. The method implements a Master-Slave distributed execution framework in the Kepler scientific workflow environment, which can realize parallel and independent execution of sub-workflows, and verifies the usability and time efficiency of the method through a use case in the theoretical ecology domain.
