一个基于DAG的卸载策略,用于IoV中的边缘计算,具有动态并行因素调整
Wenyang Guan1, Qi Zheng1, Xiaoqin Lian1
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究介绍了平行因子 (DPF) 算法的动态调整,用于汽车互联网 (IoV) 任务卸载. 通过在边缘计算环境中动态调整任务并行性,DPF优化了资源使用并减少了任务完成时间.
科学领域:
- 智能运输系统 智能运输系统
- 边缘计算是一种边缘计算.
- 网络性能优化优化 网络性能优化
背景情况:
- 汽车互联网 (IoV) 产生了大量的数据,这给计算资源分配带来了挑战.
- 动态和实时 IoV 任务特征需要适应性卸载策略超越静态方法.
研究的目的:
- 为 IoV 系统提出一个适应性的任务卸载算法.
- 在动态 IoV 环境中优化资源利用并最大限度地减少任务完成时间.
主要方法:
- 开发了一个动态调整并行因子 (DPF) 算法用于任务卸载.
- 利用边缘计算来动态调整定向非循环图 (DAG) 的平行系数.
- 实现实时监控网络条件和车辆状态,用于动态任务调度.
主要成果:
- 与静态策略相比,DPF算法显著减少了任务延迟,并提高了任务成功率.
- DPF提高了整体系统效率和资源利用率.
- 实验显示出卓越的性能,特别是在高负载场景中,任务完成时间明显更好.
结论:
- 拟议的DFF算法有效地解决了 IoV 中静态卸载的限制.
- 对任务并行性的动态调整对于优化 IoV 系统至关重要.
- 在智能运输中,DPF为高效可靠的任务卸载提供了一个有前途的解决方案.
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