基于多维特征提取和注意力融合的边缘计算任务调度机制
Shunli Zhang1,2, Jia-Ying Li1, Peng Yu2
1Department of Information Technology and Engineering, Jinzhong University, Jinzhong, People's Republic of China.
PloS one
|February 24, 2026
概括
本研究引入了一种新的边缘计算任务调度机制 (MFEAF),可以提高能源效率和任务成功率. 通过先进的网络建模,MFEAF增强了故障预测和故障耐受性调度.
科学领域:
- 边缘计算 边缘计算
- 人工智能的人工智能
- 计算机网络 计算机网络
背景情况:
- 现有的边缘计算任务调度算法面临着高能耗和中央主机低任务成功率的挑战.
- 边缘环境中主机之间的动态依赖关系是复杂的,难以建模.
研究的目的:
- 为边缘计算环境提出一个高效和适应性的任务调度机制.
- 为了提高故障预测和故障耐受性调度优化.
- 提高能源效率和任务处理能力.
主要方法:
- 开发了一个多维特征提取和注意力融合 (MFEAF) 机制.
- 集成图表注意网络和时间网络建模用于故障预测和调度.
- 采用多层图形神经网络架构,具有图形卷积和图形注意力.
- 利用动态学习速率调整和共弦回火来改善对汇率.
主要成果:
- 在故障预测方面,MFEAF获得了0.9328的F1得分,超过了现有的方法.
- 显示平均能耗下降5.0%,完成任务增加12.0%.
- 平均迁移时间减少了50% (总共19.79秒),减少了51.3%.
- 实现了集装箱的最低成本和最高公平指数,平衡了资源分配和成本效益.
结论:
- MFEAF为边缘计算中的动态故障耐受性提供了一个高效和适应性的解决方案.
- 拟议的机制显著改善了性能指标,包括故障预测,能源效率,任务处理和迁移效率.
- 在边缘环境中,MFEAF有效地平衡了资源分配和成本效益.
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