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基于模糊图神经网络的工业互联网服务组件编排方法

刘岸鑫1, 程良伦1, 王涛2   

  1. 1. 广东工业大学 计算机学院, 广东 广州 510006;
    2. 广东工业大学 自动化学院, 广东 广州 510006
  • 收稿日期:2024-05-15 出版日期:2024-10-08 发布日期:2024-10-08
  • 通信作者: 程良伦(1964–)，男，教授，博士，主要研究方向为信息物理融合系统，E-mail：llcheng@gdut.edu.cn
  • 作者简介:刘岸鑫(1997–)，男，硕士研究生，主要研究方向为工业软件，E-mail：liuanxin10337@163.com
  • 基金资助:
    国家自然科学基金联合基金资助集成项目(U20A6003)

Fuzzy Graph Neural Network-based Industrial Internet Service Component Orchestration Method

Liu An-xin1, Cheng Liang-lun1, Wang Tao2   

  1. 1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2024-05-15 Online:2024-10-08 Published:2024-10-08

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摘要： 工业互联网环境工业软件系统具有规模庞大、涵盖的软件组件与设备实体繁杂异构等特点，在异构设备与软件对象统一的服务化组件化封装基础上，通过服务组件编排可实现多样化应用工业软件系统灵活敏捷构建，但对服务编排效率与服务质量保障方面具有较高要求。为提升工业互联网环境服务组件编排效率并保障服务质量，本文提出基于图神经网络与模糊理论的服务组件编排方法FGraphSAGE_GA，将工业互联网服务组件编排问题形式化建模为图结构中的链路预测问题，并采用监督学习方法实现预测模型训练。针对新的服务组件编排问题，训练后的模型进行概率预测缩小候选空间，随后使用遗传算法优化服务组件编排方案。在两个不同的数据集上，针对3种规模的服务组件编排问题开展实验与分析，证明了所提出的FGraphSAGE_GA算法在服务质量和编排效率方面具有良好性能。

关键词: 工业互联网, 服务组件编排, 图神经网络, 模糊系统, 遗传算法

Abstract: In industrial internet environments, industrial software systems are usually large-scale and with a complex and heterogeneous array of software systems and physical devices. Based on the unified service-oriented and component-based encapsulation of heterogeneous devices and software objects, diverse applications of industrial software systems can be flexibly constructed through the orchestration of service components. However, it usually requires high efficiency of service orchestration and the assurance of service quality. To enhance the efficiency of service component orchestration and ensure service quality in industrial internet environments, this paper proposes a service component orchestration method called FGraphSAGE_GA, which formalizes the convert the component orchestration problem in industrial internet environments into a link prediction problem within a graph structure. It employs a supervised learning approach to train the prediction model. For the new service component orchestration problem, the trained model performs probabilistic predictions to narrow down the candidate space, followed by the optimization of the service component orchestration plan using a genetic algorithm. Experiments were conducted on two different datasets for service component orchestration problems of three different scales, and experimental results show that the proposed FGraphSAGE_GA algorithm has good performance in terms of service quality and orchestration efficiency.

Key words: industrial internet, service component orchestration, graph neural network, fuzzy system, genetic algorithm

中图分类号: 

  • TP393
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