期刊论文

Li, J

英文

Swarm and Evolutionary Computation

2210-6502

2024

90

101654

10.1016/j.swevo.2024.101654

Training target for teachers in Ordinary Higher Education Institutions of China; Research Foundation of Hunan Provincial Department, China [23A0209]; Science and Technology Team of Shaanxi Province, China [2023-CX-TD-07]; Program Projects in Shaanxi Province, China [2024GH-ZDXM-48]; Science Research Start Foundation for Bring in Talents of the University of Forestry and Technology, China [2019YJ005]; Science Foundation Project of Hunan Province, China [2024JJ7098]; Student Innovation and Entrepreneurship Program of the Hunan Province, China [S202310538077]; Project of Teaching Reform Research in Ordinary Higher Institutions of Hunan Province, China [HNJG-2022-0127]; R&D Project of Hunan Province, China [2022GK2025]; Provincial Key Laboratory of Intelligent Logistics Technology [2019TP1015]

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Energy consumption and makespan of workflow execution are two core performance indicators in operating cloud platforms. But, simultaneously optimizing these two indicators encounters various challenges, such as elastic resources, large-scale decision variables, and sophisticated workflow structures. To handle these challenges, we design an adaptive evolutionary scheduling algorithm, namely AESA, with three innovative strategies. First, a heuristic population initialization strategy is devised to gather workflow tasks onto limited potential reso...