期刊论文

Li, J

英文

Swarm and Evolutionary Computation

2210-6502

2024

91

101751

10.1016/j.swevo.2024.101751

Training target for young backbone teachers in Ordinary Higher Education Institutions of Hunan Province (2023); Research Foundation of Hunan Provincial Education Department [23A0209]; Science and Technology Innovation Team of Shaanxi Province [2023-CX-TD07]; Key R&D Program Projects in Shaanxi Province [2024GH-ZDXM-48]; Science Research Start Foundation for Bring in Talents of the Central South University of Forestry and Technology [2019YJ005]; Natural Science Foundation Project of Hunan Province [2024JJ5109, 2024JJ7098]; Student Innovation and Entrepreneurship Training Program of the Hunan Province [S202310538077]; Project of China Society of Logistics [2024CSLKT3-533]; Key Project of Teaching Reform Research in Ordinary Higher Education Institutions of Hunan Province [HNJG-2022-0127]; Key R&D Project of Hunan Province [2022GK2025]; Hunan Provincial Key Laboratory of Intelligent Logistics Technology [2019TP1015]; Student Innovation and Entrepreneurship Training Program of the Central South University of Forestry and Technology [2024-146]

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Cloud computing is increasingly attracting workflow applications, where workflows need to satisfy execution deadlines and energy consumption is to be minimized. So far, numerous studies have adopted evolutionary algorithms to optimize the energy consumption of workflow execution. Dynamic voltage and frequency scaling (DVFS) has been widely employed to save energy on computing devices running workflow tasks. However, most existing evolutionary algorithms focus on evolving task execution order or mapping from tasks to resources, while neglecting ...