面向5G边缘计算网络的联合需求响应与任务卸载策略

doi:10.11930/j.issn.1004-9649.202112071

中国电力 ›› 2022, Vol. 55 ›› Issue (10): 209-218.DOI: 10.11930/j.issn.1004-9649.202112071

面向5G边缘计算网络的联合需求响应与任务卸载策略

陆旭¹, 陈影¹, 许中平², 张海全¹, 慕春芳¹, 陈恺³, 孙毅³

1. 国网内蒙古东部电力有限公司调度控制调控中心，内蒙古呼和浩特　010010;
2. 北京国网信通埃森哲信息技术有限公司，北京　100080;
3. 华北电力大学电气与电子工程学院，北京　102206

收稿日期:2021-12-29 修回日期:2022-07-05 发布日期:2022-10-20
作者简介:陆旭（1970—），男，硕士，高级工程师，从事信息通信技术研究，E-mail：luxu@md.sgcc.com.cn;陈影（1984—），女，硕士，高级工程师，从事通信运行管理，E-mail：chenying@md.sgcc.com.cn;许中平（1974—），男，硕士，工程师，从事能源互联网信息技术研究，E-mail：xuzhongping@sgitg.sgcc.com.cn;张海全（1974—），男，高级工程师，从事通信运行管理，E-mail：zhanghaiquan@md.sgcc.com.cn;慕春芳（1988—），女，硕士，高级工程师，从事通信运行管理，E-mail：muchunfang@md.sgcc.com.cn;陈恺（1997—），男，通信作者，博士研究生，从事边缘计算与电力通信研究，E-mail：kulewubi@163.com;孙毅（1972—），男，教授，从事能源互联网及其信息通信技术等研究，E-mail：sy@ncepu.edu.cn
基金资助:
国家电网有限公司科技项目（SGMD0000DDJS2000351）。

Joint Demand Response and Task Offloading Strategy for 5G Edge Computing Network

LU Xu¹, CHEN Ying¹, XU Zhongping², ZHANG Haiquan¹, MU Chunfang¹, CHEN Kai³, SUN Yi³

1. State Grid East Inner Mongolia Electric Power Co., Ltd., Huhhot 010010, China;
2. Beijing SGITG-Accenture Information Technology, Beijing 100080, China;
3. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Received:2021-12-29 Revised:2022-07-05 Published:2022-10-20
Supported by:
This work is supported by Science and Technology Project of SGCC (No.SGMD0000DDJS2000351).

摘要/Abstract

摘要： 随着物联网通信计算需求激增，5G通信与边缘计算网络逐步成为新兴的高能耗负荷。针对5G边缘计算网络用电成本高的问题，提出了联合任务卸载与需求响应策略。所提策略基于任务卸载与基站储能单元充放电动作，在高电价时段联合优化计算、通信与储能资源分配或者牺牲部分时延性能以削减用电成本，在分时电价机制中为电网提供响应能力。针对优化目标为混合整数非线性规划问题，进一步提出结合网络场景划分的广义benders分解算法，将问题解耦为任务卸载决策主问题与充放电以及资源分配子问题，降低问题求解复杂度。仿真结果表明：相比于仅考虑任务卸载和仅考虑通信资源调度的响应策略，所提策略降低了用电成本并避免时延性能恶化。

关键词: 能源互联网, 5G, 边缘计算, 任务卸载, 资源分配, 需求响应

Abstract: Along with the dramatic increase in demand for IoT communication computing, the 5G communication and edge computing network gradually become the emerging high energy consumption load. In response to the high electricity bill of 5G edge computing network, this paper proposes a joint task offloading and demand response strategy for 5G edge computing network. By jointly adjusting the allocation of communication and computing resources, the task offloading decision and the charging/discharging of 5G base station battery, the proposed strategy can reduce the electric bill by transferring the task loads or sacrificing the delay performance, and provide considerable response capacity to power grid. To solve the Mixed Integer Non-linear Programming (MINLP) problem, this paper further proposes a Generalized Benders Decomposition (GBD) algorithm based on network scenario division. By decomposing the problem into a master problem of task offloading decision and a primal problem of charging/discharging and resource allocation , the complexity of the model is reduced. The simulation results show that compared with the response strategy that only considers task offloading or communication resource allocation, the proposed strategy reduces the electric bill and avoid the deterioration of delay performance.

Key words: energy Internet, 5G, edge computing, task offloading, resource allocation, demand response

陆旭, 陈影, 许中平, 张海全, 慕春芳, 陈恺, 孙毅. 面向5G边缘计算网络的联合需求响应与任务卸载策略[J]. 中国电力, 2022, 55(10): 209-218.

LU Xu, CHEN Ying, XU Zhongping, ZHANG Haiquan, MU Chunfang, CHEN Kai, SUN Yi. Joint Demand Response and Task Offloading Strategy for 5G Edge Computing Network[J]. Electric Power, 2022, 55(10): 209-218.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202112071

https://www.electricpower.com.cn/CN/Y2022/V55/I10/209

参考文献

[1] 张宁, 杨经纬, 王毅, 等. 面向泛在电力物联网的5G通信: 技术原理与典型应用[J]. 中国电机工程学报, 2019, 39(14): 4015–4024
ZHANG Ning, YANG Jingwei, WANG Yi, et al. 5G communication for the ubiquitous Internet of Things in electricity: technical principles and typical applications[J]. Proceedings of the CSEE, 2019, 39(14): 4015–4024
[2] 李彬, 贾滨诚, 陈宋宋, 等. 边缘计算在电力供需领域的应用展望[J]. 中国电力, 2018, 51(11): 154–162
LI Bin, JIA Bincheng, CHEN Songsong, et al. Prospect of application of edge computing in the field of supply and demand[J]. Electric Power, 2018, 51(11): 154–162
[3] 单葆国, 冀星沛, 姚力, 等. 能源高质量发展下中国电力供需格局演变趋势[J]. 中国电力, 2021, 54(11): 1–9,18
SHAN Baoguo, JI Xingpei, YAO Li, et al. Evolving tendency of electric supply and demand pattern under the circumstances of high-quality energy development[J]. Electric Power, 2021, 54(11): 1–9,18
[4] 中国电子信息产业发展研究院. 中国5G发展和经济社会影响白皮书(2021年) [EB/OL]. (2021-12-28)[2022-01-05]. https://mp.weixin.qq.com/mp/appmsgalbum?__biz=MjM5 MzU0 NjMwNQ==&action=getalbum&album_id=1796446442295705601#wechat_redirect.
[5] 谭萌, 彭艺, 马戎, 等. 5G对中国碳排放峰值的影响研究[J]. 中国环境科学, 2021, 41(3): 1447–1454
TAN Meng, PENG Yi, MA Rong, et al. Influence of 5G technology on the peak of China's carbon emission[J]. China Environmental Science, 2021, 41(3): 1447–1454
[6] 雍培, 张宁, 慈松, 等. 5G通信基站参与需求响应: 关键技术与前景展望[J]. 中国电机工程学报, 2021, 41(16): 5540–5552
YONG Pei, ZHANG Ning, CI Song, et al. 5G communication base stations participating in demand response: key technologies and prospects[J]. Proceedings of the CSEE, 2021, 41(16): 5540–5552
[7] 刘友波, 王晴, 曾琦, 等. 能源互联网背景下5G网络能耗管控关键技术及展望[J]. 电力系统自动化, 2021, 45(12): 174–183
LIU Youbo, WANG Qing, ZENG Qi, et al. Key technologies and prospects of energy consumption management for 5G network in background of energy Internet[J]. Automation of Electric Power Systems, 2021, 45(12): 174–183
[8] 周宸宇, 冯成, 王毅. 基于移动用户接入控制的5G通信基站需求响应[J]. 中国电机工程学报, 2021, 41(16): 5452–5462
ZHOU Chenyu, FENG Cheng, WANG Yi. Demand response of 5G communication base stations based on admission control of mobile users[J]. Proceedings of the CSEE, 2021, 41(16): 5452–5462
[9] 祝凯. 共建“5G+智能电网”生态圈[EB/OL]. (2020-06-30) [2021-10-27]. http://www.csg.cn/xwzx/2020/gsyw/202006/t20200630_311951.html.
[10] SAXENA N, ROY A, KIM H. Efficient 5G small cell planning with eMBMS for optimal demand response in smart grids[J]. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1471–1481.
[11] SHUO W, XING Z, ZHI Y, et al. Cooperative edge computing with sleep control under nonuniform traffic in mobile edge networks[J]. IEEE Internet of Things Journal, 2019, 6(3): 4295–4306.
[12] WU H M, WOLTER K, JIAO P F, et al. EEDTO: an energy-efficient dynamic task offloading algorithm for blockchain-enabled IoT-edge-cloud orchestrated computing[J]. IEEE Internet of Things Journal, 2021, 8(4): 2163–2176.
[13] CUI E F, YANG D, ZHANG H K, et al. Improving power stability of energy harvesting devices with edge computing-assisted time fair energy allocation[J]. IEEE Transactions on Green Communications and Networking, 2021, 5(1): 540–551.
[14] WANG F, XU J, CUI S G. Optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems[J]. IEEE Transactions on Wireless Communications, 2020, 19(4): 2443–2459.
[15] MUNIR M S, ABEDIN S F, TRAN N H, et al. When edge computing meets microgrid: a deep reinforcement learning approach[J]. IEEE Internet of Things Journal, 2019, 6(5): 7360–7374.
[16] PERIN G, BERNO M, ERSEGHE T, et al. Towards sustainable edge computing through renewable energy resources and online, distributed and predictive scheduling[J]. IEEE Transactions on Network and Service Management, 2022, 19(1): 306–321.
[17] LIU Y, XIE S L, YANG Q Y, et al. Joint computation offloading and demand response management in mobile edge network with renewable energy sources[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 15720–15730.
[18] CUI G M, HE Q, XIA X Y, et al. Demand response in NOMA-based mobile edge computing: a two-phase game-theoretical approach[J]. IEEE Transactions on Mobile Computing, 2021, 99: 1.
[19] CHEN X J, WEN H F, NI W, et al. Distributed online optimization of edge computing with mixed power supply of renewable energy and smart grid[J]. IEEE Transactions on Communications, 2022, 70(1): 389–403.
[20] YIN F F, ZENG M Y, ZHANG Z L, et al. Coded caching for smart grid enabled HetNets with resource allocation and energy cooperation[J]. IEEE Transactions on Vehicular Technology, 2020, 69(10): 12058–12071.
[21] CHENG Y L, ZHANG J, YANG L X, et al. Distributed green offloading and power optimization in virtualized small cell networks with mobile edge computing[J]. IEEE Transactions on Green Communications and Networking, 2020, 4(1): 69–82.
[22] SONG M, LEE Y, KIM K. Reward-oriented task offloading under limited edge server power for multiaccess edge computing[J]. IEEE Internet of Things Journal, 2021, 8(17): 13425–13438.
[23] DARAGHMEH M, AL RIDHAWI I, ALOQAILY M, et al. A power management approach to reduce energy consumption for edge computing servers[C]//2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). Rome, Italy. IEEE, 2019: 259–264.
[24] CHENY H, ZHAO D M, CHENY Q, et al. Joint computation offloading and radio resource allocations in wireless cellular networks[C]//2018 10 th International Conference on Wireless Communications and Signal Processing (WCSP). Hangzhou, China. IEEE, 2018: 1–6.
[25] 孙毅, 胡亚杰, 郑顺林, 等. 考虑用户响应特性的综合需求响应优化激励策略[J]. 中国电机工程学报, 2022, 42(4): 1402–1413
SUN Yi, HU Yajie, ZHENG Shunlin, et al. Integrated demand response optimization incentive strategy considering users' response characteristics[J]. Proceedings of the CSEE, 2022, 42(4): 1402–1413
[26] GEOFFRION A M. Generalized benders decomposition[J]. Journal of Optimization Theory and Applications, 1972, 10(4): 237–260.

面向5G边缘计算网络的联合需求响应与任务卸载策略

Joint Demand Response and Task Offloading Strategy for 5G Edge Computing Network

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	孙通, 张沈习, 曹毅, 曹佳晨, 程浩忠. 计及5G基站可调特性的配电网分布式光伏准入容量鲁棒优化[J]. 中国电力, 2025, 58(2): 140-146.
[2]	许文俊, 马刚, 姚云婷, 孟宇翔, 李伟康. 考虑绿证-碳交易机制与混氢天然气的工业园区多能优化调度[J]. 中国电力, 2025, 58(2): 154-163.
[3]	谭玲玲, 汤伟, 楚冬青, 李竞锐, 张玉敏, 吉兴全. 基于主从博弈的电热氢综合能源系统优化运行[J]. 中国电力, 2024, 57(9): 136-145.
[4]	徐峰亮, 王克谦, 王文豪, 王鹏, 王文烨, 张帅, 赵凤展. 计及激励型需求响应的低压配电网混合储能优化配置[J]. 中国电力, 2024, 57(6): 90-101.
[5]	张彩玲, 王爽, 葛淑娜, 潘登, 张岩, 韩伟, 段文岩. 计及灵活需求响应和碳-绿证交易的综合能源系统优化调度[J]. 中国电力, 2024, 57(5): 14-25.
[6]	李方姝, 余昆, 陈星莺, 华昊辰. 碳约束下基于双重博弈的电力零售商售电价格决策优化[J]. 中国电力, 2024, 57(5): 126-136.
[7]	谭玲玲, 汤伟, 楚冬青, 于子涵, 吉兴全, 张玉敏. 考虑电-氢一体化的微电网低碳-经济协同优化调度[J]. 中国电力, 2024, 57(5): 137-148.
[8]	高月芬, 员成博, 孔凡鹏, 王雪松. 需求响应激励下耦合电转气、碳捕集设备的综合能源系统优化[J]. 中国电力, 2024, 57(4): 32-41.
[9]	魏明江, 李鹏, 于浩, 冀浩然, 宋关羽, 习伟. 数字配电网边缘计算模拟实验平台[J]. 中国电力, 2024, 57(3): 12-19.
[10]	甘润东, 龙玉江, 汤杰, 何熙, 罗鸿轩, 金鑫. 计及负荷时空转移需求响应的数据中心聚合商最优运行策略[J]. 中国电力, 2024, 57(3): 20-26.
[11]	梁珩, 黄耕, 侯宾, 杨玺, 罗小虎, 张达. 工业用户连续参与需求响应的用户基线负荷精准计算方法[J]. 中国电力, 2024, 57(3): 34-42.
[12]	高志远, 庄卫金, 耿建, 李峰, 薛必克, 杨晓雷, 白柯鞠. 基于经济人假设的负荷侧资源市场化调节作用机理分析[J]. 中国电力, 2024, 57(3): 213-223.
[13]	唐志辉, 张欣, 宁艺飞, 黄璜, 余昆, 华昊辰, 曹佳伟, 郑云天. 基于联盟链技术的商业建筑转供电主体需求响应策略[J]. 中国电力, 2024, 57(12): 109-119.
[14]	夏向阳, 谭欣欣, 单周平, 李辉, 徐志强, 吴晋波, 岳家辉, 陈贵全. 储能电站锂离子电池本体安全关键技术及新技术应用情况[J]. 中国电力, 2024, 57(11): 1-17.
[15]	李铁成, 范辉, 张卫明, 王献志, 张艺宏, 戴志辉. 基于5G通信的有源配电网新能源送出线路纵联保护[J]. 中国电力, 2024, 57(11): 139-150.

模态框（Modal）标题

面向5G边缘计算网络的联合需求响应与任务卸载策略

Joint Demand Response and Task Offloading Strategy for 5G Edge Computing Network

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics