在工业互联网中,多服务器边缘计算的任务卸载具有联合负载平衡和模糊安全性
Xiaomin Jin1,2,3, Shuai Zhang4,5,6, Yurong Ding4,5,6
1Xi'an University of Posts and Telecommunications, School of Computer Science and Technology, Xi'an, 710121, China. xmjin@xupt.edu.cn.
Scientific reports
|November 13, 2024
概括
边缘计算 (EC) 通过卸载任务来增强工业终端. 本研究介绍了一个延迟意识的多服务器部分卸载模型和算法,用于优化工业互联网性能.
科学领域:
- 事物的工业互联网 (IIoT)
- 边缘计算 边缘计算
- 任务卸载 任务卸载
背景情况:
- 工业终端在工业互联网中面临着延迟敏感和计算密集型任务的挑战.
- 边缘计算 (EC) 通过将任务卸载到相邻的边缘服务器来增加计算能力提供了一个解决方案.
研究的目的:
- 为了应对在工业互联网中为EC制定准确的卸载策略的挑战.
- 研究延迟意识的多服务器部分EC任务卸载问题,结合负载平衡和安全性.
主要方法:
- 开发了一个任务卸载模型,支持部分卸载,多服务器分布,负载平衡和模糊风险评估.
- 制定了模型作为一个受约束的优化问题,证明了它的NP硬度.
- 建议使用自适应性遗传算法和模拟化粒子群集优化进行双层卸载算法,以实现联合负载平衡和模糊安全性.
主要成果:
- 既定模型有效地降低了目标价值,与全边执行相比降低了27%,与本地执行相比降低了46%.
- 拟议的双层卸载算法显示出比现有方法更高的解决方案准确性.
结论:
- 拟议的延迟感知部分卸载模型和算法对工业互联网有效.
- 该方法成功地平衡了减缓延迟,负载平衡和任务卸载中的安全考虑.
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