Predictive State-Aware Deep Reinforcement Learning With Hyper-Heuristic for Resolving Conflicting Objectives in Scientific Workflow Scheduling

Scientific workflows in cloud environments are highly complex and dynamic, necessitating intelligent and flexible scheduling solutions to handle important factors, including resource heterogeneity, budget constraints, deadlines, and ever-changing workload requirements. In contrast to traditional sch...

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Κύριοι συγγραφείς:	Awadh Salem Bajaher, Nor Asilah Wati Abdul Hamid, Idawaty Ahmad, Zurina Mohd Hanapi
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	IEEE 2025-01-01
Σειρά:	IEEE Access
Θέματα:	Cloud computing scientific workflow tasks workflow scheduling deadline budget hyper-heuristic generation
Διαθέσιμο Online:	https://ieeexplore.ieee.org/document/11186249/
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