基于拉格朗日的计算迁移能耗优化策略

doi:10.11959/j.issn.1000-0801.2018295

Abstract

Abstract:

With the development of mobile network technology and the popularization and application of intelligent terminals,mobile edge computing has become an important application of cloud computing.Computing offloading strategy has become one of the key issues in mobile edge computing services.Targeting the total computing time of the mobile terminal and minimizing the energy consumption of the mobile terminal,the computation offloading resource allocation problem of the mobile terminal was modeled as a convex optimization problem,solved by the Lagrange multiplier method,and a threshold-based offloading was proposed.Simulation experiments show that the proposed offloading optimization strategy model can effectively balance the relationship between local computing and offloading,and provide guarantee for executing computation-intensive applications in mobile edge computing.

Key words: edge computing, computation offloading, energy consumption, computing time, Lagrangian

CLC Number:

TP393

Guangxue YUE,Youkang ZHU,Jiansheng LIU,Yasheng DAI,Zhenxu YOU,Hao XU. Optimizing strategy of computing off loading energy consumption based on Lagrangian method[J]. Telecommunications Science, 2018, 34(12): 10-23.

Figures/Tables 14

符号	含义
K	系统中终端用户总数量
k	第k个终端用户
T	迁移总时隙
D_k	终端k某个任务的数据量
d_k	终端k任务迁移的数据量
C_c	边缘云上计算1 bit数据所需的CPU周期数
C_k	终端k计算1 bit数据所需的CPU周期数
H	信道增益
σ	高斯白噪声的方差
p_k	终端k的传输功率
B	系统信道带宽
r_k	数据传输速率
$f_{k}^{(l)}$	终端k的计算能力（即每秒的CPU周期数）
$f_{k}^{(e)}$	边缘云的计算能力（即MEC服务器每秒的周期数）
t_k	分配给终端k的迁移时隙
a_k	迁移决策约束变量
e_k	终端k本地计算每个CPU周期的能耗
w_k	终端k对应的拉格朗日乘子

References 28

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评价指标	云计算	边缘计算
部署方式	集中式	分布式
与用户的距离	远	近
延迟	高	低
抖动	大	小
计算能力	强	有限
存储能力	强	有限

研究成果	发表年份	实现方法	设计目标
CloudAware^[8]	2016年	优化迁移策略	加速计算、节能
Replisom^[9]	2016年	克隆虚拟机、压缩	减少响应时间
Femtoclouds^[10]	2015年	优化迁移策略	增强计算能力
ME-VoLTE^[11]	2015年	优化传输	降低能耗
EAB^[12]	2015年	优化传输	加速资源访问
上下文感知协作实时应用^[13]	2015年	上下文感知	在实时场景中降低延迟
端到端计算迁移^[14]	2014年	优化迁移策略	提高整个网络资源利用效率

参考文献	发表年份	创新点	存在不足
15]	2013年	比较本地计算和迁移计算的能量消耗，得出最优迁移策略	仅通过对比迁移前后的能量消耗无法得到最优的迁移策略，仅研究了单用户情况
16]	2016年	以无线能量传输为目标,设计了基于无缝集成移动云计算的解决方案	仅研究了低复杂度设备（如传感器），对于高计算复杂度的智能终端并不适用
17]	2016年	以无线能量采集为目标，设计出了基于微波功率传输的解决方案	仅考虑CPU频率和发射功率的优化，忽略了计算任务
18]	2014年	将动态迁移与自适应的 LTE/Wi-Fi 链接选择进行集成，提出了一种可扩展的近似动态规划算法	注重于单用户的算法设计，缺乏全局最优考量
19]	2015年	将无线和计算资源进行联合优化	缺乏严密的计算延迟考量
20]	2015年	研究了迁移到不同云的最优用户调度问题，提出了一个基于任务负载的阈值迁移策略	仅关注于延迟问题，没有涉及研究终端能耗
21]	2015年	研究了多用户分布式计算迁移，运用博弈论来实现能量和延迟的最小化	分布式的计算迁移算法难以达到全局最优
24]	2018年	基于马尔可夫决策和动态规划算法建立最优策略	计算复杂度高
27]	2018年	使用智能分区和动态迁移研究了迁移计算中的用户隐私安全问题	缺乏对富应用程序复杂性的考虑
28]	2018年	设计基于启发式搜索、重构线性化技术和半定松弛3种算法来实现延迟和可靠性的权衡	缺乏对能耗的优化

参数	数值
系统中终端用户总数量(K)	(20,40,60,80,100)
迁移总时隙(T)	20 ms
终端k某个任务的数据量(D_k)	100~500 KB
边缘云上计算1比特数据所需的CPU周期数(C_c)	200个周期/比特
终端k计算1比特数据所需的CPU周期数(C_k)	500~1 500个周期/比特
高斯白噪声的方差(σ)	10^-9W
系统信道带宽(B)	10 MHz
终端k的传输功率p_k	100 mW
终端k的计算能力(即每秒的CPU周期数)( f ^(l)k )	1~10 GHz
边缘云的计算能力(即MEC服务器每秒的周期数)( f ^(e)k )	100 GHz
终端k本地计算每个CPU周期的能耗(e_k)	0~2×10^-10J/转

Optimizing strategy of computing off loading energy consumption based on Lagrangian method

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References 28

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