面向配电网多业务资源智能调度的边缘容器迁移优化方法

doi:10.11930/j.issn.1004-9649.202302062

中国电力 ›› 2023, Vol. 56 ›› Issue (9): 120-126.DOI: 10.11930/j.issn.1004-9649.202302062

• 配电网规划与优化运行 • 上一篇下一篇

面向配电网多业务资源智能调度的边缘容器迁移优化方法

李帅¹, 徐迪², 文祥宇¹, 张嘉鑫³

1. 国网山东省电力公司电力科学研究院，山东济南 250003;
2. 国网山东省电力公司济南供电公司，山东济南 250013;
3. 武汉大学电气与自动化学院，湖北武汉 430070

收稿日期:2023-02-17 修回日期:2023-08-03 发布日期:2023-09-20
作者简介:李帅(1992-),男,硕士,中级工程师,从事配网数字化和配电物联网研究,E-mail:1257646262@qq.com;徐迪(1992-),女,硕士,中级工程师,从事配用电技术研究,E-mail:zdxudi@sina.com;文祥宇(1994-),男,硕士,中级工程师,从事配网数字化和配电物联网研究,E-mail:1446816847@qq.com;张嘉鑫(1998-),女,博士研究生,从事配用电数字化研究,E-mail:2020282070157@whu.edu.cn
基金资助:
国家重点研发计划资助项目（配电网业务资源协同及互操作关键技术，2021YFB2401300）。

An Edge Container Migration Optimization Method for Multi-service Intelligent Resource Scheduling of Distribution Networks

LI Shuai¹, XU Di², WEN Xiangyu¹, ZHANG Jiaxin³

1. State Grid Shandong Electric Power Research Institute, Jinan 250003, China;
2. Jinan Electric Power Corporation of State Grid Shandong Electric Power Corporation, Jinan 250013, China;
3. School of Electrical and Automation, Wuhan University, Wuhan 430070, China

Received:2023-02-17 Revised:2023-08-03 Published:2023-09-20
Supported by:
This work is supported by the National Key Research and Development Program of China (Key Technologies for Coordination and Interoperation of Power Distribution Service Resource, No.2021YFB2401300).

摘要/Abstract

摘要： 边缘容器迁移技术可实现业务数据从资源受限服务器到资源丰富服务器的智能调度。基于容器迁移技术，面向配电网多业务资源调度场景，建立容器迁移容量与时延增益模型，并构建加权时延增益最大化问题。提出边缘容器动态匹配迁移优化方法，通过价格增量的自适应调整，实现对业务优先级、迁移容量、时延需求的感知。仿真结果表明，相较于传统方法，所提方案可有效提升配电网业务处理时延性能。

关键词: 配电网, 边缘容器迁移, 动态匹配, 资源智能调度

Abstract: Edge container migration technology can achieve intelligent scheduling of service data from resource-constrained servers to resource-sufficient servers. Based on the container migration technology, the container migration capacity, delay gain model and the weighted delay gain maximization problem are formulated for the distribution network multi-service resource scheduling scenarios. An edge container dynamic matching migration optimization method is proposed, which can realize the perception of the service priority, migration capacity, and latency requirements through the adaptive adjustment of the price increment. The simulation results demonstrate that compared with traditional algorithms, the proposed method can validly improve the data processing delay performance for distribution network.

Key words: distribution network, edge container migration, dynamic matching, intelligent resource scheduling

李帅, 徐迪, 文祥宇, 张嘉鑫. 面向配电网多业务资源智能调度的边缘容器迁移优化方法[J]. 中国电力, 2023, 56(9): 120-126.

LI Shuai, XU Di, WEN Xiangyu, ZHANG Jiaxin. An Edge Container Migration Optimization Method for Multi-service Intelligent Resource Scheduling of Distribution Networks[J]. Electric Power, 2023, 56(9): 120-126.

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参考文献

[1] 谭鸣骢, 王玲玲, 蒋传文, 等. 考虑负荷聚合商调节潜力的需求响应双层优化模型[J]. 中国电力, 2022, 55(10): 32–44
TAN Mingcong, WANG Lingling, JIANG Chuanwen, et al. Bi-level optimization model of demand response considering regulation potential of load aggregator[J]. Electric Power, 2022, 55(10): 32–44
[2] 金国彬, 刘玉龙, 李国庆, 等. 考虑可靠性的交直流混合配电网网架与分布式电源协同优化规划[J]. 电力系统保护与控制, 2022, 50(22): 59–70
JIN Guobin, LIU Yulong, LI Guoqing, et al. Collaborative optimization planning of an AC/DC hybrid distribution network frame and distributed power generation considering reliability[J]. Power System Protection and Control, 2022, 50(22): 59–70
[3] ZHOU X, ZOU S L, WANG P, et al. ADMM-based coordination of electric vehicles in constrained distribution networks considering fast charging and degradation[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(1): 565–578.
[4] MAO M M, XU J J, WU Z J, et al. A multiarea state estimation for distribution networks under mixed measurement environment[J]. IEEE Transactions on Industrial Informatics, 2022, 18(6): 3620–3629.
[5] 翁嘉明, 刘东, 安宇, 等. 馈线功率控制下的主动配电网信息物理风险演化分析[J]. 中国电力, 2021, 54(3): 12–22
WENG Jiaming, LIU Dong, AN Yu, et al. Cyber-physical risk evolution analysis of active distribution network under feeder control error[J]. Electric Power, 2021, 54(3): 12–22
[6] 叶宇剑, 袁泉, 汤奕. 面向双碳目标的交通网-电网耦合网络中电动汽车负荷低碳优化方法[J]. 中国电力, 2023, 56(5): 72–79
YE Yujian, YUAN Quan, TANG Yi. Electric vehicle charging demand low carbon optimization in traffic-grid coupling networks towards“dual carbon”Goal[J]. Electric Power, 2023, 56(5): 72–79
[7] 陈亚鹏, 曲睿, 贾璐瑞, 等. 面向区域能量调控的信息时效性保障与数据价值提升策略[J/OL]. 中国电机工程学报, 2023: 1–13.[2023-09-12]. DOI: 10.13334/j.0258-8013.pcsee.222841.
CHEN Yapeng, QU Rui, JIA Lurui, et al. Information timeliness guarantee and data value enhancement strategy for regional energy regulation[J/OL]. Proceedings of the CSEE, 2023: 1–13. [2023-09-12]. DOI: 10.13334/j.0258-8013.pcsee.222841.
[8] 孙强, 孙志凰, 潘杭萍, 等. 考虑多种储能的数据中心综合能源系统配置优化[J]. 中国电力, 2022, 55(9): 1–7
SUN Qiang, SUN Zhihuang, PAN Hangping, et al. Configuration optimization of integrated energy system for data center considering multiple energy storage facilities[J]. Electric Power, 2022, 55(9): 1–7
[9] GUIM F, METSCH T, MOUSTAFA H, et al. Autonomous lifecycle management for resource-efficient workload orchestration for green edge computing[J]. IEEE Transactions on Green Communications and Networking, 2022, 6(1): 571–582.
[10] MA L L, YI S H, CARTER N, et al. Efficient live migration of edge services leveraging container layered storage[J]. IEEE Transactions on Mobile Computing, 2019, 18(9): 2020–2033.
[11] ALEYADEH S, MOUBAYED A, HEIDARI P, et al. Optimal container migration/Re-instantiation in hybrid computing environments[J]. IEEE Open Journal of the Communications Society, 2022, 3: 15–30.
[12] LV L, ZHANG Y C, LI Y S, et al. Communication-aware container placement and reassignment in large-scale Internet data centers[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(3): 540–555.
[13] LONG S Q, WEN W, LI Z T, et al. A global cost-aware container scheduling strategy in cloud data centers[J]. IEEE Transactions on Parallel and Distributed Systems, 2022, 33(11): 2752–2766.
[14] RANJAN R, THAKUR I S, AUJLA G S, et al. Energy-efficient workflow scheduling using container-based virtualization in software-defined data centers[J]. IEEE Transactions on Industrial Informatics, 2020, 16(12): 7646–7657.
[15] KAEWKASI C, CHUENMUNEEWONG K. Improvement of container scheduling for Docker using ant colony optimization[C]//2017 9th International Conference on Knowledge and Smart Technology (KST). Chonburi, Thailand. IEEE, 2017: 254–259.
[16] 罗艺, 江凌云. 移动边缘计算环境下容器实时迁移方法[J]. 通信技术, 2022, 55(5): 599–604
LUO Yi, JIANG Lingyun. A live migration method for containers in mobile edge computing environments[J]. Communications Technology, 2022, 55(5): 599–604
[17] SUN Z J, YANG H, LI C, et al. Cloud-edge collaboration in industrial Internet of Things: a joint offloading scheme based on resource prediction[J]. IEEE Internet of Things Journal, 2022, 9(18): 17014–17025.
[18] 王小雪, 王晓锋, 刘渊. 基于OpenStack的高资源利用率Docker调度模型[J]. 计算机工程, 2022, 48(9): 171–179, 196
WANG Xiaoxue, WANG Xiaofeng, LIU Yuan. OpenStack-based Docker scheduling model with high resource utilization[J]. Computer Engineering, 2022, 48(9): 171–179, 196
[19] 施超, 谢在鹏, 柳晗, 等. 基于稳定匹配的容器部署策略的优化[J]. 计算机科学, 2018, 45(4): 131–136
SHI Chao, XIE Zaipeng, LIU Han, et al. Optimization of container deployment strategy based on stable matching[J]. Computer Science, 2018, 45(4): 131–136
[20] SWAIN C, SAHOO M N, SATPATHY A, et al. METO: matching-theory-based efficient task offloading in IoT-fog interconnection networks[J]. IEEE Internet of Things Journal, 2021, 8(16): 12705–12715.
[21] 周振宇, 王曌, 廖海君, 等. 电力物联网5G云–边–端协同框架与资源调度方法[J]. 电网技术, 2022, 46(5): 1641–1651
ZHOU Zhenyu, WANG Zhao, LIAO Haijun, et al. 5G cloud-edge-end collaboration framework and resource scheduling method in power Internet of Things[J]. Power System Technology, 2022, 46(5): 1641–1651
[22] CHOUDHURY S, MAHESHWARI S, SESKAR I, et al. ShareOn: shared resource dynamic container migration framework for real-time support in mobile edge clouds[J]. IEEE Access, 2022, 10: 66045–66060.
[23] LIAO H J, ZHOU Z Y, ZHAO X W, et al. Learning-based context-aware resource allocation for edge-computing-empowered industrial IoT[J]. IEEE Internet of Things Journal, 2020, 7(5): 4260–4277.
[24] ZHANG H Q, XIAO Y, BU S R, et al. Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining stackelberg game and matching[J]. IEEE Internet of Things Journal, 2017, 4(5): 1204–1215.
[25] FAN Y Q, WANG L F, WU W L, et al. Cloud/edge computing resource allocation and pricing for mobile blockchain: an iterative greedy and search approach[J]. IEEE Transactions on Computational Social Systems, 2021, 8(2): 451–463.

面向配电网多业务资源智能调度的边缘容器迁移优化方法

An Edge Container Migration Optimization Method for Multi-service Intelligent Resource Scheduling of Distribution Networks

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面向配电网多业务资源智能调度的边缘容器迁移优化方法

An Edge Container Migration Optimization Method for Multi-service Intelligent Resource Scheduling of Distribution Networks

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