ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (4): 699-708.doi: 10.7544/issn1000-1239.2020.20190882

所属专题: 2020数据驱动网络专题

• 网络技术 • 上一篇    下一篇

异构YANG模型驱动的网络领域知识图谱构建

董永强1,3,王鑫1,刘永博1,杨望2,3   

  1. 1(东南大学计算机科学与工程学院 南京 211189);2(东南大学网络空间安全学院 南京 211189);3(计算机网络和信息集成教育部重点实验室(东南大学) 南京 211189) (dongyq@seu.edu.cn)
  • 出版日期: 2020-04-01
  • 基金资助: 
    国家自然科学基金项目(61971131);国家重点研发计划项目(2018YFB1800205)

Building Network Domain Knowledge Graph from Heterogeneous YANG Models

Dong Yongqiang1,3, Wang Xin1, Liu Yongbo1, Yang Wang2,3   

  1. 1(School of Computer Science and Engineering, Southeast University, Nanjing 211189);2(School of Cyber Science and Engineering, Southeast University, Nanjing 211189);3(Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 211189)
  • Online: 2020-04-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61971131) and the National Key Research and Development Program of China (2018YFB1800205).

摘要: 随着网络规模持续扩大,复杂且异构的网络环境给网络的自动化配置管理带来了严峻的挑战,现有的网络智能化运维方案缺少知识层面的统一数据模型,难以有效进行网络大数据处理.YANG作为一种数据建模语言,用于对网络配置管理协议NETCONF传输的配置与状态数据进行建模.提出一种YANG模型驱动的网络领域知识图谱构建方案,该方案基于YANG语言规范,提出网络知识本体构建的基本原则,形成包含51个类、70余种属性的本体结构;随后对来自不同标准化组织和厂商的异构YANG模型,进行数据抽取和实例化生成单源知识图谱,进而利用YANG模型之间存在的异构共指特性,采用实体对齐方法建立模型间的语义映射关系,形成网络领域知识图谱.该知识图谱可为网络运维大数据的生成与维护提供统一的语义框架,无须再进行手工的运维本体构建,从而极大地简化网络的配置管理与运行维护,为网络性能优化和异常检测等运维难题提供新的解决思路.

关键词: 异构网络, 智能网络运维, 本体, 知识图谱, YANG模型

Abstract: With the continuous expansion of network scale, network management and operation face great challenges of complexity and heterogeneity. The existing intelligent network operation approaches lack a unified data model at the knowledge level to guide the process of network big data. As a data modeling language, YANG has been used to model the configuration and state data transmitted by NETCONF protocol. This paper proposes an intelligent network operation scheme which builds network domain knowledge graph from heterogeneous YANG models. As per YANG language specification, the scheme proposes the basic principles of network domain ontology construction, forming an ontology structure containing 51 classes and more than 70 properties. Then, the heterogeneous YANG models from different standardization organizations and vendors are extracted and instantiated into network domain knowledge graph. Entity alignment methods are therein employed to explore the semantic co-reference relationships among uni-source YANG models. The acquired knowledge graph provides a unified semantic framework to organize massive network operation data, which thus eliminates the requirement to construct AIOps ontology manually. As such, the configuration management and operational maintenance of networks could be greatly simplified, enlightening new solutions for network performance optimization and anomaly detection problems.

Key words: heterogeneous network, intelligent network operation, ontology, knowledge graph, YANG models

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