Lightweight software-defined edge computing network topology discovery method

ZHOU Qizhao; SHI Zhongyu

doi:10.11959/j.issn.2096-109x.2025071

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Lightweight software-defined edge computing network topology discovery method

Papers | 更新时间：2026-01-28

- Lightweight software-defined edge computing network topology discovery method
- Chinese Journal of Network and Information Security Vol. 11, Issue 6, Pages: 144-156(2025)
- 作者机构：
  
  1.成都信息工程大学计算机学院，四川成都 610225
  2.成都职业技术学院，四川成都 610041
- 作者简介：
- 基金信息：
  
  The Opening Project from the State Key Laboratory of Heavy-Duty and Express High-Power Electric Locomotive(QZKFKT2025-06);The Opening Project from the State Key Laboratory of Hydraulics and Mountain River Engineering(SKHL2413)
- DOI：10.11959/j.issn.2096-109x.2025071
  CLC： TN915.08
- Received：08 March 2025，
  
  Revised：2025-09-10，
  
  Published：25 December 2025
- 稿件说明：
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周启钊, 石中煜. 轻量级软件定义边缘计算网络拓扑发现方法[J]. 网络与信息安全学报, 2025, 11(6): 144-156.

ZHOU Qizhao, SHI Zhongyu. Lightweight software-defined edge computing network topology discovery method[J]. Chinese Journal of Network and Information Security, 2025, 11(6): 144-156.
周启钊, 石中煜. 轻量级软件定义边缘计算网络拓扑发现方法[J]. 网络与信息安全学报, 2025, 11(6): 144-156. DOI： 10.11959/j.issn.2096-109x.2025071.

ZHOU Qizhao, SHI Zhongyu. Lightweight software-defined edge computing network topology discovery method[J]. Chinese Journal of Network and Information Security, 2025, 11(6): 144-156. DOI： 10.11959/j.issn.2096-109x.2025071.

摘要

现有软件定义边缘计算网络（SD-ECN）已实现高效、可靠、安全的网络通信，但面对物联网、人工智能、大数据等不同规模、不同需求的网络环境，其拓扑架构的弹性和可扩展性仍存在不足。在软件定义网络中，开放流发现协议（OFDP）通过封装链路层发现协议（LLDP）包，由控制器发送和接收相关的报文，并解析生成网络拓扑。边缘设备通过高速链路相互连接形成边缘网络，并通过一个或多个汇聚节点连接到核心网络。在差异化技术网络环境需求下，边缘节点的更新率通常较高，而核心骨干交换机则无须重复更新。在此背景下，现有软件定义边缘计算网络拓扑发现方法存在冗余链路查找和拓扑重构效率低的问题，最终导致控制器不堪重负、边缘计算任务卸载机制失效。为此，提出了一种轻量级软件定义边缘计算网络拓扑发现方法。针对冗余链路查找问题，通过多级流表规则和LLDP帧优化实现了数据包的动态绑定、动态验证和实时转发；针对控制器在拓扑发现过程中下发报文数量过多的问题，提出了一种基于最小顶点覆盖思想的边缘节点和链路发现算法；针对控制器接收报文数量过多问题，提出了一种动态单双向边缘链路解析与合并算法。实验结果表明，与现有网络拓扑发现方法相比，所提方法在实现拓扑重构过程中解析链路容量和节点间的连接关系功能的基础上实现了轻量级的网络拓扑发现，有效减少了网络数据包的数量和控制器的负载。

Abstract

Software-defined edge computing networks enable efficient

reliable and secure network communication. The topological architecture of software-defined edge computing networks requires flexibility and scalability to adapt to the technological environments including Internet of things

artificial intelligence and big data. Edge computing devices are interconnected through high-speed links

core network and converging nodes. In software-defined networks

the OpenFlow discovery protocol (OFDP) utilizes link layer discovery protocol (LLDP) packets for sending and receiving related messages and generating network topology. The edge node update rate is typically high under different scales and requirements while the core backbone convergence switches do not require repeated updates. Existing topology discovery schemes for software-defined edge computing networks consequently exhibit low efficiency in redundant link identification and topology reconstruction

ultimately causing controller overload and affecting edge computing task offloading mechanisms. To resolve these issues

a lightweight topology discovery method for software-defined edge computing networks was proposed. For redundant link discovery challenges

dynamic packet binding

verification and real-time forwarding were implemented through multi-level flow table rules and optimized LLDP frame protocols. For excessive message transmission during topology discovery in SDN controllers

an edge node and link discovery algorithm based on minimum vertex cover concept was developed. Additionally

an MVC-based dynamic single/double-directional edge link resolution and merging algorithm was introduced to alleviate message overload in SDN controllers. Experimental results demonstrated that compared with existing network topology discovery algorithms

the proposed method achieved lightweight network topology discovery while maintaining capabilities for resolving link capacities and inter-node connections during reconstruction. This approach effectively reduces data packet volume within the network and decreases controller workload.

关键词

Keywords

references

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