A decomposition-based multi-objective evolutionary algorithm with reinforcement learning for workflow scheduling in cloud computing environment

Cloud computing has become an integral part of modern computer science. Cloud service providers (CSPs) often have multiple conflicting objectives for different user requirements. Thus, workflow scheduling in cloud computing environment is a challenge multi-objective optimization problem (MOP). The m...

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Veröffentlicht in:	Cluster computing Jg. 28; H. 10; S. 678
Hauptverfasser:	Xue, Fei, Wen, Jinbu, Wang, Peiwen, Fan, Wenyu, Geng, Yuge, Dong, Tingting
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2025 Springer Nature B.V
Schlagworte:	Cloud computing Completion time Computer Communication Networks Computer Science Convergence Decomposition Efficiency Evolutionary algorithms Genetic algorithms Heuristic Machine learning Multiple objective analysis Operating Systems Optimization Optimization algorithms Parameter sensitivity Processor Architectures Scheduling User requirements Workflow Cloud computing MOEA Workflow scheduling Reinforcement learning
ISSN:	1386-7857, 1573-7543
Online-Zugang:	Volltext
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