北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2023, Vol. 46 ›› Issue (2): 37-42.

• 算力网络与分布式云 • 上一篇    下一篇

异构蜂窝网络中基于雾节点协作贡献度的计算卸载算法

黄龙杨1,张楠2,刘笑笑2,申滨2   

  1. 1. 中国民用航空飞行学院
    2. 重庆邮电大学
  • 收稿日期:2022-02-02 修回日期:2022-06-05 出版日期:2023-04-28 发布日期:2023-05-14
  • 通讯作者: 申滨 E-mail:shenbin@cqupt.edu.cn
  • 基金资助:
    国家自然科学基金项目;民航发展基金项目

Fog Node Contribution Degree Based Task Offloading Algorithm for Heterogeneous Cellular Network

  • Received:2022-02-02 Revised:2022-06-05 Online:2023-04-28 Published:2023-05-14
  • Contact: Bin -SHEN E-mail:shenbin@cqupt.edu.cn
  • Supported by:
    The project is partly supported by the National Natural Science Foundation of China (NSFC);Civil Aviation Development Fund

摘要: 雾计算(Fog Computing)将基于云的服务拓展至无线网络边缘,能够与多种场景联合部署。针对密集异构蜂窝网络雾计算系统中的协作计算卸载问题,为充分且合理地利用所有雾节点的计算资源,本文提出一种基于雾节点协作贡献度的计算卸载算法。首先对协作可行性、协作公平性和协作稳定性进行了建模设计;其次,在其基础上定义了协作贡献度与协作贡献比系数。然后,结合雾节点的剩余计算容量阈值与协作贡献度阈值给出了协作雾节点筛选算法。最后,在满足任务可容忍的最大时延约束下,提出以最小化任务执行能耗与用户支付成本的加权和为目标的优化问题,并结合外部罚函数法与方向加速法(Powell法)得到最优卸载决策。仿真结果表明,所提算法在各种任务参数和时延约束下,相对于多种对比算法,能够有效降低执行任务的总开销,并且能够对协作可行性、协作公平性和协作稳定性进行权衡处理。

关键词: 密集异构蜂窝网络, 雾计算, 计算卸载, 协作贡献度

Abstract: Fog computing extends the cloud-based service to the network edge and it can be deployed in various scenarios. Aiming to solve the problem of fog computation task offloading in dense heterogeneous cellular network, and make full and reasonable utilization of the computing resources of all fog nodes, this paper proposes a computation task offloading algorithm. Firstly, the feasibility, fairness and stability of fog node cooperation are modeled and designed. Secondly, the contribution degree and contribution ratio coefficient of cooperation are defined. Combined with the threshold of the remaining computing capacity and the threshold of the cooperative contribution degree of the fog nodes, a cooperative fog node selection algorithm is proposed. Finally, an optimization problem is proposed to minimize the weighted sum of the task execution energy consumption and the user's payment cost under the constraint of the maximum tolerable delay of the task, and the optimal unloading decision is obtained by combining the external penalty function method and Powell (direction acceleration) method. Simulation results show that the proposed algorithm can effectively reduce the total cost of dense heterogeneous cellular network, compared with some of the algorithms studied in this paper.

Key words: dense heterogeneous cellular network, fog computing, computation offloading, the contribution degree of cooperation

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