在车辆Ad-Hoc网络中的多云环境资源分配模型的增强新方法
R Augustian Isaac1, P Sundaravadivel2, V S Nici Marx1
1Saveetha Engineering College, Chennai, Tamilnadu, India.
Scientific reports
|March 20, 2025
概括
本研究介绍了一种优化的资源分配方法,用于使用交叉和突变中心的海洋捕食者算法 (CM-MPA) 的车辆边缘计算 (VEC) 网络. 该方法通过有效地安排资源以满足不断增长的应用需求来确保服务质量 (QoS).
科学领域:
- 车载云网络 车载云网络
- 边缘计算 边缘计算
- 资源分配 资源分配
背景情况:
- 越来越多的服务请求在车辆网络压力有限的资源,阻碍服务质量 (QoS).
- 有效的调度和资源管理对于车辆边缘计算 (VEC) 满足应用需求至关重要.
研究的目的:
- 为VEC网络引入创新的资源分配策略.
- 优化资源调度和利用,以确保QoS保证.
- 为满足车辆环境中应用日益增长的需求.
主要方法:
- 从车辆网络模型中提取特征 (移动性,带宽等). ) 的情况.
- 使用大象群狮子优化器 (EHLO) 进行关键属性识别.
- 使用修改后的模糊C-Means (MFCM) 进行车辆集群.
- 实施一个以交叉和突变 (CM) 为中心的海洋捕食者算法 (MPA) 来分配资源.
主要成果:
- CM-MPA有效地分配资源,优化VEC网络内的调度.
- 该方法证明了高效的车辆聚类和属性优先级.
- 在 MATLAB 中的模拟验证了拟议方法的性能.
结论:
- 拟议的基于CM-MPA的资源分配为VEC网络提供了一个全面的解决方案.
- 这一进步确保了在应用需求不断增长的情况下提供 QoS 保证.
- 这项研究对车辆边缘计算的高效资源管理做出了重大贡献.
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