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<p>Cloud platforms have been widely used in many fields, and their energy consumption has become an important issue. Similar to other cloud environments, medical cloud platforms consume substantial energy. Tasks with different deadlines pose challenges for task scheduling in medical clouds. This paper presents three deadline types and uses a benefit function to quantify the effect of different completion times on task utility. We aim to reduce energy consumption, improve benefits, minimize completion time, and decrease the size of files transferred between clouds. Based on this analysis, a Grey Wolf Optimizer-based multi-objective scheduling method (GWOS) is proposed for resource scheduling in medical clouds. Simulation results demonstrate that GWOS outperforms the compared methods across all four scheduling objectives.</p>

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A multi-objective scheduling method based on the Grey Wolf Optimizer in medical cloud computing

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Min Zhao,
  • Jingkai Zeng,
  • Liqun Yang

摘要

Cloud platforms have been widely used in many fields, and their energy consumption has become an important issue. Similar to other cloud environments, medical cloud platforms consume substantial energy. Tasks with different deadlines pose challenges for task scheduling in medical clouds. This paper presents three deadline types and uses a benefit function to quantify the effect of different completion times on task utility. We aim to reduce energy consumption, improve benefits, minimize completion time, and decrease the size of files transferred between clouds. Based on this analysis, a Grey Wolf Optimizer-based multi-objective scheduling method (GWOS) is proposed for resource scheduling in medical clouds. Simulation results demonstrate that GWOS outperforms the compared methods across all four scheduling objectives.