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
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.
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.
Fig.1Composition of electricity information management system in residential area
Fig.2Information 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.1Parameter setting in task unload framework
Fig.3Process 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.2Each 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.3Parameter 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.4Terminal 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.5Game result of task unloading
Fig.4Change curve of parameters with game time
Fig.5Trend of number of beneficial unload tasks
Fig.6Trend 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.6Influence of energy consumption and time delay ratio on unloading decision
Fig.7Comparison of load results of three calculation methods
Fig.8Load results of different calculation modes under different preference settings
Fig.9Experimental comparison results with and without AECN
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