对移动边缘计算任务卸载在空间-空中-地面集成网络中的风险意识分布强大的优化.
Zhiyuan Li1,2,3, Pinrun Chen1
1School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
我们介绍了RADROO,这是一个用于移动边缘计算 (MEC) 集成的太空空地网络 (SAGIN) 中任务卸载的新算法. RADROO增强了决策的稳定性,可以应对网络的不确定性,从而提高智能应用程序的性能.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 优化理论 优化理论
背景情况:
- 太空空地面综合网络 (SAGIN) 提供全球连接,但面临着移动边缘计算 (MEC) 资源限制的挑战.
- 由于移动设备上的计算和存储不足,智能应用程序的体验质量不佳.
研究的目的:
- 在SAGIN集成的MEC环境中解决最佳任务卸载决策问题.
- 克服诸如节点功能的波动,不确定的传输延迟和可变任务负载等挑战.
主要方法:
- 提出了一种新的"在风险意识分布强大的优化条件值" (RADROO) 算法.
- 集成的分布性强大的优化与条件价值风险模型,以实现最佳的任务卸载.
- 在模拟的SAGIN环境中对RADROO进行了评估,并与各种参数进行了比较,并与最先进的算法进行了比较.
主要成果:
- 在网络不确定性下,RADROO在解决任务卸载决策方面表现强.
- 实验结果证实RADROO能够实现非最佳的移动任务卸载决策.
- 在稳定性方面,RADROO的表现优于标准的强大优化,随机优化,DRO和Brute算法.
结论:
- 在复杂的SAGIN-MEC环境中,RADROO为任务卸载提供了强大的解决方案.
- 拟议的算法有效地处理了SAGIN网络固有的不确定性.
- RADROO为移动任务卸载提供了改进的决策,提高了智能应用程序的性能.
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