基于李雅普诺夫随机优化的车辆边缘计算资源管理
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作者单位:

1.河南科技大学;2.东北大学

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TN929.53

基金项目:

河南省高校科技创新团队支持计划(18IRTSTHN011); 中原科技创新领军人才资助项目(194200510012); 河南省高等学校重点科研项目(20A120008); 河南省自然科学基金(202300410149); 国家“十三五”装备预研领域基金(61403120207, 61402100203); 河南省科技攻关项目(202102310200)


Resource Management of Vehicle Edge Computing Based on Lyapunov Stochastic Optimization
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Affiliation:

1.Henan University of Science and Technology;2.Northeastern University

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    摘要:

    针对车辆边缘计算系统中的计算资源管理问题,提出一种基于李雅普诺夫随机优化的计算卸载与资源分配方案。构建在保证任务量及长期能耗约束下的车辆用户服务时延最小化优化问题。利用李雅普诺夫随机优化理论将优化问题分解。在本地计算资源分配子问题中,通过求解线性问题的方法,得到最优本地计算 CPU 频率;在计算卸载子问题中,利用数值优化求解得到最优发射功率。最后,借助李雅普诺夫随机优化中的漂移惩罚方法,设计出一种低复杂度的联合计算卸载与资源分配算法,通过同时控制卸载决策、本地计算 CPU 频率和计算卸载的发射功率,实现整个车辆边缘计算系统中车辆用户的服务时延最小,提高了车辆边缘计算服务质量。仿真结果验证了所提算法的有效性。

    Abstract:

    In order to solve the problem of computing resource management in the vehicle edge computing system, a computational offloading and resource allocation scheme based on Lyapunov stochastic optimization is proposed. Constructs the optimization problem of minimizing the service delay of vehicle users under the constraints of guaranteed task load and long-term energy consumption. Use Lyapunov stochastic optimization theory to decompose the optimization problem. In the local computing resource allocation sub-problem, the optimal local computing CPU frequency is obtained by solving the linear problem; in the computing offloading sub-problem, the optimal transmit power is obtained by numerical optimization. Finally, with the help of the drift penalty method in Lyapunov stochastic optimization, a low-complexity joint computing offloading and resource allocation algorithm is designed. The entire vehicle is realized by simultaneously controlling offloading decision-making, locally calculating CPU frequency, and calculating offloading transmit power. The service delay of vehicle users in the edge computing system is the smallest, which improves the service quality of vehicle edge computing. The simulation results verify the effectiveness of the proposed algorithm.

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历史
  • 收稿日期:2020-08-31
  • 最后修改日期:2020-12-07
  • 录用日期:2020-12-25
  • 在线发布日期: 2021-02-04
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