面向新型电力系统的最小服务延迟的负载分配算法研究

张秋雨; 蒋云峰; 张常稳; 张一鸣

张秋雨,蒋云峰,张常稳,张一鸣.面向新型电力系统的最小服务延迟的负载分配算法研究[J].电测与仪表,2022,59(1):1-9.
ZHANG Qiuyu,JIANG Yunfeng,ZHANG Changwen,ZHANG Yiming.Research on Load Distribution Algorithm for Minimum Service Delay in New Power System[J].Electrical Measurement & Instrumentation,2022,59(1):1-9.

面向新型电力系统的最小服务延迟的负载分配算法研究

Research on Load Distribution Algorithm for Minimum Service Delay in New Power System

DOI：10.19753/j.issn1001-1390.2022.01.001

中文关键词: 边缘计算新型电力系统低时延负载分配

英文关键词:edge computing new power system low latency load distribution

基金项目:中央高校基本科研业务费专项资金资助项目(2014MS87)；国网河北省电力有限公司资助：基于物联网技术的配网设备状态监测技术研究及系统开发 (5204XT20000N)；国家自然科学基金资助项目(61501185)

作者中文名	作者英文名	单位
张秋雨	ZHANG Qiuyu	国网河北省电力有限公司邢台供电分公司
蒋云峰	JIANG Yunfeng	国网河北省电力有限公司邢台供电分公司
张常稳	ZHANG Changwen	国网河北省电力有限公司邢台供电分公司
张一鸣	ZHANG Yiming	华北电力大学科技学院;华北电力大学科技学院

摘要点击次数: 1804

中文摘要:

边缘计算依托其在网络边缘的技术和部署优势,能够实时采集终端设备的运行情况,并按需完成数据云台的上传,有效缓解了云端处理器的工作压力,降低数据实时回传的延时。然而,资源有限的单一边缘节点的固有特性可能依然无法满足大规模接入的新型电力业务的延迟要求。对此本文研究了满足较低服务延迟的工作负载分配策略。考虑到网络时延和计算时延构建了一种基于多个边缘计算节点的工作负载分配模型,以最小化新型电力系统场景下的服务时延。此外,为进一步降低多业务终端的服务时延,制定了一种工作负载分配机制。在初始化阶段,通过任务平衡算法来改善边缘节点间的工作负载平衡。为了有效保证资源分配的合理性,采用一种通过改进的粒子群优化算法,实现边缘节点的CPU资源的合理分配。基于半定规划算法,将网络资源做到更有效的分配。最终通过仿真实验,表明了本文所提的分配策略更加有效。

英文摘要:

Relying on its technology and deployment advantages at the edge of the network, edge computing can collect the running status of terminal equipment in real time, and complete the upload of data to the cloud platform on demand, effectively alleviating the working pressure of the cloud processor and reducing the delay of real-time data return. However, the inherent characteristics of a single edge node with limited resources may still be unable to meet the delay requirements of large-scale access power Internet of Things services. In this regard, this paper studies the workload distribution that satisfies lower service latency. Considering the network delay and computing delay, a workload distribution model based on multiple edge computing nodes is constructed to minimize the service delay in the power Internet of Things scenario. In addition, in order to further reduce the service delay of multi-service terminals, a workload distribution mechanism has been developed. In the initialization phase, the task balancing algorithm is used to improve the workload balance among edge nodes. An improved particle swarm optimization algorithm is proposed to allocate the CPU resources of edge nodes. In task allocation, based on a semi-definite planning algorithm, application tasks are allocated to edge nodes to achieve a reasonable allocation of network resources. Finally, the simulation results verify the effectiveness of the task allocation strategy proposed in this paper.

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