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面向配电台区感知的边缘终端资源调度技术

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在多用户接入的边缘计算环境中存在设备和容器之间的资源调度问题.本文的主要目的是研究和优化边缘终端资源调度,以提高系统性能和资源利用效率.为了实现这一目标,对边缘终端资源调度进行了建模,并提出了一种基于盖尔-沙普利(GS)算法的优化方案.首先,通过建立边缘计算环境下的资源调度模型分析了设备和容器之间的匹配关系;然后,采用GS算法进行资源分配,通过设备和任务之间的双向匹配实现了资源的最佳分配.为了验证所提出方案的有效性,本文进行了仿真实验,模拟测试了不同任务数量和设备数量情况下的系统性能.实验结果显示,在任务数量增加和设备数量超过容器数量时,GS算法能够有效降低系统开铕,与传统的资源分配算法相比,表现出显著的优势.此外,本文还提出了应对计算复杂性问题的策略,如并行计算和智能任务分配,以提高算法的可扩展性和适应性.
Edge Terminal Resource Scheduling Technology for Distribution Station Area Sensing
In a multi-user edge computing environment,there is a resource scheduling issue between devices and containers.The main objective of this paper is to study and optimize edge terminal resource scheduling to improve system performance and resource utilization efficiency.In order to achieve this goal,edge terminal resource scheduling is modeled and an optimization scheme based on the Gale-Shapley(GS)algorithm is proposed.Firstly,the matching relationship between devices and containers is analyzed by establishing a resource scheduling model in the edge computing environment.Then,GS algorithm is used to allocate resources,and the optimal allocation of resources is realized through bidirectional matching between devices and tasks.In order to verify the effectiveness of the proposed scheme,simulation experiments are conducted to simulate and test the system performance under the condition of different numbers of tasks and devices.Experimental results show that the GS algorithm can effectively reduce system overhead when the number of tasks increases and the number of devices exceeds the number of containers,demonstrating significant advantages compared with traditional resource allocation algorithm.In addition,This paper also proposes strategies to address computational complexity issues,such as parallel computing and intelligent task allocation,to improve the scalability and adaptability of the algorithm.

edge computingresource schedulingmulti-user system managementparallel computingperformance optimization

姚磊、赵文慧、董天强、欧新、娄金和、王钰

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贵州电网有限责任公司贵阳供电局,贵州贵阳 550001

边缘计算 资源调度 多用户系统管理 并行计算 性能优化

2024

电力大数据
贵州电力试验研究院 贵州省电机工程学会

电力大数据

影响因子:0.047
ISSN:2096-4633
年,卷(期):2024.27(10)