面向电工智慧物联的服务缓存和计算卸载策略

doi:10.11930/j.issn.1004-9649.202110035

中国电力 ›› 2022, Vol. 55 ›› Issue (4): 23-32,43.DOI: 10.11930/j.issn.1004-9649.202110035

• 新型电力系统信息安全：理论、技术与应用 • 上一篇下一篇

面向电工智慧物联的服务缓存和计算卸载策略

孙毅¹, 常少南¹, 陈恺¹, 崔强², 沈维捷³

1. 华北电力大学电气与电子工程学院，北京 102206;
2. 国网物资有限公司，北京 100120;
3. 国网上海市电力公司，上海 200122

收稿日期:2021-10-18 修回日期:2021-12-28 出版日期:2022-04-28 发布日期:2022-04-24
作者简介: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;常少南（1998—），男，通信作者，硕士研究生，从事任务卸载技术研究，E-mail：changshaonan@sina.cn;陈恺（1997—），男，博士研究生，从事云边协同及任务卸载技术研究，E-mail：kulewubi@163.com;崔强（1985—），男，博士研究生，从事物资供应链管理，E-mail：cuiqiang@sgm.sgcc.com.cn;沈维捷（1965—），男，高级工程师，从事物资供应链管理，E-mail：shenwj@sh.sgcc.com.cn
基金资助:
国家电网有限公司科技项目（面向工业互联网的电工装备智慧物联体系研究与应用，52090020002W）。

Joint Service Caching and Computing Offloading Strategies for Electrical Equipment Intelligent IoT Platform

SUN Yi¹, CHANG Shaonan¹, CHEN Kai¹, CUI Qiang², SHEN Weijie³

1. Department of Electrical Engineering, North China Electric Power University, Beijing 102206, China;
2. State Grid Materials Co., Ltd., Beijing 100120, China;
3. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China

Received:2021-10-18 Revised:2021-12-28 Online:2022-04-28 Published:2022-04-24
Supported by:
This work is supported by Science & Technology Project of SGCC (Research and Application of Intelligent IoT System of Electrical Equipment for Industrial Internet, No.52090020002W).

摘要/Abstract

摘要： 海量数据接入对电工装备智慧物联发起挑战，亟须边缘计算协助数据实时处理。现有工作重点研究了任务卸载技术在工业互联网场景的应用，未充分关注服务缓存对任务卸载策略的影响。针对上述问题，提出了面向电工装备智慧物联场景的联合服务缓存与任务卸载策略。针对供应商与服务提供商的收益冲突问题，以供应商数据处理时延最小化为目标，研究与证明了计算服务提供商与供应商之间的主从博弈以及纳什均衡点的存在，提出结合粒子群的广义Benders分解算法实现问题求解。仿真结果表明，所提策略有效提高了任务处理效率，降低了供应商的计算成本，提高了服务提供商的服务收益。

关键词: 电工装备, 边缘计算, 任务卸载, 服务缓存, 成本优化, 工业互联网

Abstract: Mass data constitutes a challenge to the electrical equipment intelligent industrial Internet of Things (EIP), and it is urgently needed for edge computing to assist the real-time processing of data. Current works mainly focus on the application of edge computing in Industrial Internet of Things(IIoT), while the impact of service caching strategy on edge computing is not paid enough attention. Aiming to solve above problem, a joint service caching and task offloading strategy for EIP was proposed. In order to solve the revenue conflict between service providers and equipment suppliers, we investigated and proved the existence of a two-stage Stackelberg game and the Nash equilibrium(NE). Further more, we proposed a generalized Benders decomposition algorithm combined with particle swarm optimization to solve the mixed integer programming efficiently. The simulation results show that the proposed strategy can effectively improve the task processing efficiency, reduce the computing cost of suppliers, and improve the service revenue of the service providers.

Key words: electrical equipment, edge computing, task offloading, service caching, cost optimization, industrial Internet

孙毅, 常少南, 陈恺, 崔强, 沈维捷. 面向电工智慧物联的服务缓存和计算卸载策略[J]. 中国电力, 2022, 55(4): 23-32,43.

SUN Yi, CHANG Shaonan, CHEN Kai, CUI Qiang, SHEN Weijie. Joint Service Caching and Computing Offloading Strategies for Electrical Equipment Intelligent IoT Platform[J]. Electric Power, 2022, 55(4): 23-32,43.

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面向电工智慧物联的服务缓存和计算卸载策略

Joint Service Caching and Computing Offloading Strategies for Electrical Equipment Intelligent IoT Platform

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Joint Service Caching and Computing Offloading Strategies for Electrical Equipment Intelligent IoT Platform

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