缓存辅助边缘计算的卸载决策与资源优化

doi:10.13190/j.jbupt.2019-189

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (3): 32-37.doi: 10.13190/j.jbupt.2019-189

缓存辅助边缘计算的卸载决策与资源优化

薛建彬, 丁雪乾, 刘星星

兰州理工大学计算机与通信学院, 兰州 730050

收稿日期:2019-09-19 出版日期:2020-06-28 发布日期:2020-06-24
作者简介:薛建彬(1973-),男,教授,E-mail:xue_jabn@hotmail.com.
基金资助:
国家自然科学基金项目（61841107，61461026）

Offloading Decision and Resource Optimization for Cache-Assisted Edge Computing

XUE Jian-bin, DING Xue-qian, LIU Xing-xing

School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China

Received:2019-09-19 Online:2020-06-28 Published:2020-06-24
Supported by:

摘要/Abstract

摘要： 提出一种缓存辅助边缘计算的卸载决策制定与资源优化方案，以进一步降低移动边缘计算（MEC）系统中终端设备的能量消耗.首先，建立该优化问题为最小化用户在任务执行时最坏情况下的能耗值，并将这一混合整数规划问题转化为非凸的二次约束二次规划（QCQP）模型，使用半定松弛及随机概率映射方法获得缓存辅助下的预选卸载集合；其次，分别采用拉格朗日对偶分解法和二分法求得性能约束下的最优传输功率及边缘计算资源分配，从而通过对比该集合中的设备能耗得到理想的卸载决策集合与资源分配方案.实验数值结果表明，所提方案能够有效降低用户能量消耗，提升边缘计算系统的服务性能.

关键词: 移动边缘计算, 缓存, 计算卸载, 半定松弛, 资源分配

Abstract: An offloading decision and resource optimization scheme for cache-assisted edge computing is proposed to further reduce the energy consumption of terminal devices in the mobile egde computing(MEC)system.Firstly,the optimization problem is established to minimize the worst-case energy consumption of user during the task execution,and the mixed integer programming problem is transformed into a non-convex quadratic constrained quadratic programming(QCQP)model.Semidefinite-relaxation and randomization probability mapping are used to obtain the pre-selected offloading set assisted by caching;Secondly, the Lagrangian dual decomposition method and the bisection method are utilized to acquire the optimal transmission power and edge computing resource under constraints.By comparing the energy consumption of the set of devices,an ideal set of offloading decision and resource allocation scheme are got.Experiment shows that the proposed scheme can effectively reduce the energy consumption and improve the service performance of the edge computing system.

Key words: mobile edge computing, caching, computational offloading, semidefinite-relaxation, resource allocation

中图分类号:

TN929.5

薛建彬, 丁雪乾, 刘星星. 缓存辅助边缘计算的卸载决策与资源优化[J]. 北京邮电大学学报, 2020, 43(3): 32-37.

XUE Jian-bin, DING Xue-qian, LIU Xing-xing. Offloading Decision and Resource Optimization for Cache-Assisted Edge Computing[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 32-37.

参考文献

[1] 田辉, 范绍帅, 吕昕晨, 等. 面向5G需求的移动边缘计算[J]. 北京邮电大学学报, 2017, 40(2):1-10. Tian Hui, Fan Shaoshuai, Lu Xinchen, et al. Mobile edge computing for 5G requirements[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(2):1-10.
[2] Zhang Jiao, Hu Xiping, Ning Zhaolong, et al. Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks[J]. IEEE Internet of Things Journal, 2017, 5(4):2633-2645.
[3] Yang Lichao, Zhang Heli, Li Ming, et al. Mobile edge computing empowered energy efficient task offloading in 5G[J]. IEEE Transactions on Vehicular Technology, 2018, 67(7):6398-6409.
[4] Guo Junfeng, Song Zhaozhe, Cui Ying, et al. Energy-efficient resource allocation for multi-user mobile edge computing[C]//IEEE Global Communications Conference (GLOBECOM). Singapore:IEEE Press, 2017:1-7.
[5] 张海波, 李虎, 陈善学, 等. 超密集网络中基于移动边缘计算的任务卸载和资源优化[J]. 电子与信息学报, 2019, 41(5):1194-1201. Zhang Haibo, Li Hu, Chen Shanxue, et al. Computing offloading and resource optimization in ultra-dense networks with mobile edge computation[J]. Journal of Electronics and Information, 2019, 41(5):1194-1201.
[6] Jia Qingming, Xie Renchao, Tao Huang, et al. Efficient caching resource allocation for network slicing in 5G core network[J]. IET Communications, 2017, 11(18):2792-2799.
[7] Jiang Wei, Feng Gang, Qin Shuang. Optimal cooperative content caching and delivery policy for heterogeneous cellular networks[J]. IEEE Transactions on Mobile Computing, 2016, 16(5):1382-1393.
[8] Wang Shuo, Zhang Xin, Wang Lin, et al. Joint design of device to device caching strategy and incentive scheme in mobile edge networks[J]. IET Communications, 2018, 12(14):1728-1736.
[9] Du Jianbo, Zhao Liqiang, Feng Jie, et al. Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee[J]. IEEE Transactions on Communications, 2018, 66(4):1594-1608.
[10] Chen Meng-HSi, Dong Min, Liang Ben. Resource sharing of a computing access point for multi-user mobile cloud offloading with delay constraints[J]. IEEE Transactions on Mobile Computing, 2018, 17(12):2868-2881.
[11] Cui Ying, He Wen, Ni Chun, et al. Energy-efficient resource allocation for cache-assisted mobile edge computing[C]//2017 IEEE 42nd Conference on Local Computer Networks (LCN). Singapore:IEEE Press, 2017:640-664.

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缓存辅助边缘计算的卸载决策与资源优化

Offloading Decision and Resource Optimization for Cache-Assisted Edge Computing

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