基于人工智能的数据网络故障管理系统研究

Yunzhou Dong, Xinyu Wang, Fangyou Fu, Zhengdong Lin, Chaona Yin, Yingkun Liao, Peng Lin
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引用次数: 0

摘要

SDN、NFV等技术增加了数据网络系统的复杂性,导致网络故障的概率增加,维护难度加大。为了设计更实用的故障管理框架和机制,首先对数据网络环境进行了分析。根据数据网络环境和网元的特点,提出了一种基于人工智能的故障管理体系结构。该体系结构包括设备层、数据采集层、数据分析层和数据管理层。为了提高故障管理体系结构的应用价值和便捷性,详细设计了数据管理层的弹性策略、自愈策略和工单分发机制。在性能分析中,从实施可行性和性能方面验证了本文提出的故障管理机制具有良好的应用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Fault Management System based on Artificial Intelligence in Data Network
SDN, NFV and other technologies increase the complexity of data network systems, resulting in an increase in the probability of network failures and the difficulty of maintenance. In order to design a more practical fault management framework and mechanism, the data network environment is analyzed first. Based on the characteristics of data network environment and network elements, a fault management architecture based on artificial intelligence is proposed. The architecture includes device layer, data acquisition layer, data analysis layer and data management layer. In order to improve the application value and convenience of the fault management architecture, the elastic strategy, self-healing strategy and work order distribution mechanism of the data management layer are designed in detail. In the performance analysis, from the implementation feasibility and performance aspects, it is verified that the fault management mechanism proposed in this paper has good application value.
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