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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (5): 917-926    DOI: 10.3785/j.issn.1008-973X.2021.05.012
    
Optimization strategy for unloading power tasks in residential areas based on alternate edge nodes
Zhong CHEN1(),Xiao XU1(),Hai-wei WANG2,Hong-hao LUO1,Xuan CHEN3
1. School of Electrical Engineering, Southeast University, Nanjing 210096, China
2. Hefei Power Supply Company, State Grid Anhui Electric Power Co. Ltd, Hefei 230000, China
3. Maintenance Branch, State Grid Jiangsu Electric Power Co. Ltd, Nanjing 211102, China
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Abstract  

In order to propel power consumption orderly and ensure the security of electricity distribution system in residential area, the concept of task offloading in the communication field was expanded and applied to residential area information management, the electricity information management system in residential area and the optimal offloading strategy of computing tasks based on edge calculation were also proposed. The definition of edge computing was clarified, the concept of task offloading was extended from the mobile edge computing scenario, as well as the role of alternate edge node was added in the residential area electricity information management model. The framework and process of task offloading were proposed, at the same time, the calculation tasks generated by electrical equipment in residential areas were analyzed. Then, the Nash equilibrium was achieved to obtain the optimal task offloading decision by establishing a computing model and a multi-user game model. An example was used to verify the necessity of the alternate edge node and the superiority of the proposed strategy compared with the traditional computing model. The proposed strategy brings new ideas and methods for the data processing in the residential area electricity information management in the era of Internet of all things.

Key words： edge computing      task offloading      residential area      electricity information management      multi-user game      delay      energy consumption     
Received: 24 July 2020      Published: 10 June 2021
CLC:  TM 73  
Fund:  国家电网总部资助项目（SGJSJX00YJJS1800722）
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Zhong CHEN
Xiao XU
Hai-wei WANG
Hong-hao LUO
Xuan CHEN
Cite this article:

Zhong CHEN,Xiao XU,Hai-wei WANG,Hong-hao LUO,Xuan CHEN. Optimization strategy for unloading power tasks in residential areas based on alternate edge nodes. Journal of ZheJiang University (Engineering Science), 2021, 55(5): 917-926.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.05.012     OR     http://www.zjujournals.com/eng/Y2021/V55/I5/917


基于备用边缘节点的居民区用电任务卸载优化策略

为了实现有序用电，保证居民区配电系统安全性，将通信领域的任务卸载概念拓展并应用于居民区信息管理中，提出基于边缘计算的居民区用电信息管理系统和计算任务的优化卸载策略. 阐明边缘计算的相关定义，从移动边缘计算场景中拓展任务卸载的概念，并在居民区用电信息管理模型中增加备用边缘节点角色. 提出基于任务卸载的管理框架及流程，并对居民区用电设备产生的计算任务进行分析，通过建立计算模型和多用户博弈模型，求解纳什均衡，得到最优任务卸载决策. 用算例验证备用边缘节点的必要性以及所提策略相较于传统计算模式的优越性，为万物互联时代的居民区用电信息管理中的数据处理环节带来新的思路和方法.


关键词： 边缘计算,  任务卸载,  居民区,  用电信息管理,  多用户博弈,  时延,  能耗 
Fig.1 Composition of electricity information management system in residential area
Fig.2 Information interaction framework of residential area
终端类型 产生任务的数据量/
kB 		产生任务的计算量/
cycles 		计算结果量/
kB 		计算能力/
GHz 		CPU功率/
W 		计算内存 对于时延和
能耗的敏感度
终端设备（ECN） $b(n) $ $ d(n) $ $ r(n) $ $ f(n) $ $ c(n) $ ? $ \lambda_{\rm{t}} (n) $、 $ \lambda_{\rm{e}} (n)$
备用边缘节点（AECN） ? ? ? $ f(m) $ $ c(m) $ $k(m) $ ?
云平台 ? ? ? $ f(c) $ $ c(c) $ ? ?
Tab.1 Parameter setting in task unload framework
Fig.3 Process of task unloading optimization
终端设
备序号 		$ c(n)$/W $b(n) $/kB $d(n) $/cycles $r(n) $/kB $f(n) $/GHz ${\lambda _{\rm{t}}}(n)$ ${\lambda _{\rm{e}}}(n)$
1 4 1000 3000 25 10 0 1.0
2 5 500 1000 20 5 0.2 0.8
3 6 2000 4000 250 12 0.5 0.5
4 7 1500 2000 2000 8 0.8 0.2
5 7 2000 4000 250 12 1.0 0
6 6 500 1000 20 5 0.2 0.8
7 5 1000 3000 25 10 0.8 0.2
8 4 2000 4000 250 12 0.5 0.5
9 5 500 1000 20 5 0.2 0.8
10 4 1500 2000 2000 8 0.8 0.2
11 6 1000 3000 25 10 0.5 0.5
12 5 500 1000 20 5 0.5 0.5
13 7 1500 2000 2000 8 1.0 0
14 5 500 1000 20 5 0 1.0
15 4 1000 3000 25 10 0.5 0.5
$\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $
Tab.2 Each terminal parameter of ECN
参数 数值
备用边缘节点计算能力/GHz 15
备用边缘节点容量/kB 6×106
信道带宽/MHz 1
移动设备发射功率/W 2
移动设备接收功率/W 2
背景噪音/dBm ?80
信道增益 0.9
云平台计算能力/GHz 40
云平台容量/kB 5×108
MEC与中心云数据传输速率/Mbps 106
云端处理CPU单位功率/W 15
云端发射功率/W 2
云端设备接收功率/W 2
Tab.3 Parameter of AECN and cloud platform
类型 终端
数量 		须上传
数据量/kB 		任务计算
量/（105
cycles） 		结果数
据量/kB 		计算能
力/（106
cycles）
分布式电源 10 1000 3000 25 500
电动汽车 16 500 1000 20 800
储能设备 13 800 2000 100 600
家庭电力信息 11 2000 4000 250 1200
Tab.4 Terminal parameter for different types of computing tasks
An Xn An Xn An Xn An Xn
1 1 1 1 ?1 1 5 1
2 1 ?1 1 ?1 1 ?1 1
?1 1 1 1 ?1 1 ?1 1
?1 1 4 1 1 1 3 1
?1 1 ?1 1 ?1 1 2 1
4 1 2 1 ?1 1 ?1 1
?1 1 ?1 1 ?1 1 1 1
?1 1 4 1 4 1 ?1 1
3 1 ?1 1 2 1 ?1 1
?1 1 1 1 5 1 3 1
?1 1 ?1 1 5 1 ?1 1
3 1 ?1 1 ?1 1 ? ?
?1 1 ?1 1 ?1 1 ? ?
Tab.5 Game result of task unloading
Fig.4 Change curve of parameters with game time
Fig.5 Trend of number of beneficial unload tasks
Fig.6 Trend of number of users selecting to unload task
$\lambda_{\rm{e}} $∶ $\lambda_{\rm{t}} $ Noff $\lambda_{\rm{e}} $∶ $\lambda_{\rm{t}} $ Noff
1∶0 0 4∶6 34
7∶3 12 3∶7 47
6∶4 16 0∶1 50
5∶5 22 ? ?
Tab.6 Influence of energy consumption and time delay ratio on unloading decision
Fig.7 Comparison of load results of three calculation methods
Fig.8 Load results of different calculation modes under different preference settings
Fig.9 Experimental comparison results with and without AECN
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