计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (11A): 240200010-7.doi: 10.11896/jsjkx.240200010

• 网络&通信 • 上一篇    下一篇

基于端边协同的节点部署和资源分配联合优化方法

杨哲铭1,3, 左路路1,2,3, 纪雯1,2   

  1. 1 中国科学院计算技术研究所 北京 100190
    2 鹏城实验室 广东 深圳 518055
    3 中国科学院大学计算机科学与技术学院 北京 100049
  • 出版日期:2024-11-16 发布日期:2024-11-13
  • 通讯作者: 纪雯(jiwen@ict.ac.cn)
  • 作者简介:(yangzheming19b@ict.ac.cn)
  • 基金资助:
    国家重点研发计划(2023YFB4502805);国家自然科学基金(62072440);北京市自然科学基金(L221004)

Joint Optimization Method for Node Deployment and Resource Allocation Based on End-EdgeCollaboration

YANG Zheming1,3, ZUO Lulu 1,2,3, JI Wen 1,2   

  1. 1 Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
    2 Pengcheng Laboratory,Shenzhen,Guangdong 518055,China
    3 School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:YANG Zheming,born in 1996,doctoral student.His main research interests include multimedia systems,edge intelligence,network optimization,and visual IoT.
    JI Wen,born in 1976,Ph.D,professor,Ph.D supervisor.Her main research interests include vision processing units,end-edge-cloud computing systems,vision coding and transmission,intelligent multimedia computing,low-carbon computing,and optimization theory methods.
  • Supported by:
    National Key R & D Program of China(2023YFB4502805),National Natural Science Foundation of China(62072440) and Beijing Natural Science Foundation(L221004).

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摘要： 随着物联网技术的快速发展,边缘计算作为一种重要的数据处理方式,在多样化的应用场景中展现出其独特的优势。在这种背景下,边缘服务器的位置部署和资源分配成为了提高任务处理效率的关键因素。然而,这一过程面临着终端设备的广泛分布和边缘服务器的异构特性带来的显著挑战。为了有效解决这些问题,提出了一种基于端边协同的节点部署和资源分配联合优化方法,旨在全面提升边缘计算系统的整体性能。首先,利用分层聚类算法,根据终端设备在功能和地理位置上的相似性,将它们有效地划分为若干个区域。随后,根据边缘服务器的处理能力、存储空间和网络带宽等关键指标,决定每个区域内最适合的边缘节点。最后,通过联合优化节点部署和资源利用率来指导任务的分配情况。为了验证所提方法的有效性,在公开数据集上对不同方法进行了仿真实验。实验结果表明,相比现有方法,所提方法可以将负载均衡水平提升30％以上,并将任务处理的时延和能耗降低10％以上,这在提升边缘计算系统的可持续性和效率方面具有重要意义。

关键词: 边缘计算, 节点部署, 资源分配, 负载均衡, 任务卸载

Abstract: With the rapid development of IoT technology,edge computing shows its unique advantages in diverse application scenarios.The location deployment and resource allocation of edge servers become the key factors to improve the efficiency of task processing.However,this process faces significant challenges due to the wide distribution of end devices and the heterogeneous nature of edge servers.To effectively address these issues,this paper proposes a joint optimization method for node deployment and resource allocation based on end-edge collaboration,aiming to improve the overall performance of edge computing systems comprehensively.Our approach first utilizes a hierarchical clustering algorithm to effectively divide end devices into several regions based on their functional and geographic similarities.Subsequently,based on key metrics such as processing power,storage space,and network bandwidth of edge servers,the most suitable edge nodes in each region is decided.Finally,allocating tasks is guided by jointly optimizing node deployment and resource utilization.To validate the effectiveness of the proposed method,we conduct simulation experiments of different methods on public datasets.Experimental results show that our proposed method can improve the load balancing level by more than 30% and reduce task processing latency and energy consumption by more than 10%,compared to the existing methods.

Key words: Edge computing, Node deployment, Resource allocation, Load balancing, Task offloading

中图分类号: 

  • TP 311.5
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