Collaborative task offloading and resource allocation optimization for intelligent edge devices

Xian LI; Suzhi BI; Hongru ZENG; Bin LIN; Xiaohui LIN

doi:10.11959/j.issn.2096-3750.2022.00303

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Collaborative task offloading and resource allocation optimization for intelligent edge devices

Theory and Technology | 更新时间：2024-08-16

- Collaborative task offloading and resource allocation optimization for intelligent edge devices
- Chinese Journal on Internet of Things Vol. 6, Issue 4, Pages: 41-52(2022)
- 作者机构：
  
  1. 深圳大学电子与信息工程学院，广东深圳 518060
  2. 鹏城实验室宽带通信研究部，广东深圳 518066
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2022.00303
  CLC： TN92
- Online First：2022-12，
  
  Published：30 December 2022
- 稿件说明：
移动端阅览
Xian LI, Suzhi BI, Hongru ZENG, et al. Collaborative task offloading and resource allocation optimization for intelligent edge devices[J]. Chinese Journal on Internet of Things, 2022, 6(4): 41-52.
DOI：

Xian LI, Suzhi BI, Hongru ZENG, et al. Collaborative task offloading and resource allocation optimization for intelligent edge devices[J]. Chinese Journal on Internet of Things, 2022, 6(4): 41-52. DOI： 10.11959/j.issn.2096-3750.2022.00303.

摘要

为了解决移动边缘计算网络中计算资源日益紧缺的问题，设计了一种基于用户协作的边缘计算资源分配机制，充分利用用户之间的空闲计算资源，有效提升系统整体的数据处理性能。以最大化用户的效用函数为目标，将目标优化问题建模为一个关于用户任务卸载决策和本地计算通信资源的联合优化问题，并结合深度学习技术和凸优化理论，提出了一种混合深度学习-优化算法对目标问题进行求解。仿真结果表明，相较于对比算法，所提算法能使用户的效用提升至少85.4%，并能在亚秒级的时间内实现用户效用的近似最优化。

Abstract

In order to deal with the increasingly scarce computing resources

a cooperative edge computing scheme was proposed

which makes full use of the idle resources among users to improve the overall data processing performance.To maximize the user utility

the target problem was formulated as an MINLP (mixed integer non-linear programming)

and a learning-optimization-integrated method was proposed to jointly optimize the resource allocation and user offloading decisions.Simulation results show that the proposed scheme can produce a near-optimal solution in sub-second and effectively improve the system utility at least 85.4% compared to the considered benchmark methods.

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references

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