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人工智能和物联网驱动的监控和可视化,以优化多租户网络中的MSP操作:采用传感器数据集成的模块化方法

Adeel Rafiq¹, Muhammad Zeeshan Shakir², David Gray³

¹School of Engineering, Computing and Mathematical Sciences, University of Wolverhampton, Wolverhampton WV1 1LY, UK.

Sensors (Basel, Switzerland)

|October 16, 2025

概括

本研究引入了用于网络监控的AI和物联网框架,增强了管理服务提供商 (MSP) 的预测异常检测,并减少了停机时间. 该系统大大缩短了事件解决时间和运营成本.

科学领域:

计算机科学计算机科学
网络工程网络工程
人工智能的人工智能

背景情况:

托管服务提供商 (MSP),特别是中小企业 (SMEs),在各种客户基础设施的预测网络可见性方面扎.
当前的网络监控解决方案往往缺乏集成的预测分析和边缘智能功能.
实现多租户意识监控仍然是MSP管理分布式客户端网络的重大挑战.

研究的目的:

开发和验证人工智能和物联网驱动的预测网络监控框架.
提高管理多租户环境的MSP的可见性和异常检测.
通过主动警报和边缘情报来减少网络停机时间和事件解决时间.

主要方法:

将边缘物联网节点 (树派普罗米修斯模块) 与用于数据收集和分析的机器学习模型集成.
使用Prometheus,Grafana和Mimir进行数据管理,可视化和存储.
实现简单线性回归 (SLR),K-Means聚类和长短期记忆 (LSTM) 模型,用于预测异常检测和故障分类.
在边缘或中央部署AI模块的容器化部署,并将风险指标反到Prometheus.
使用VPN道和基于令牌的身份验证来安全地隔离租户,确保GDPR合规.

主要成果:

在五个MSP客户端 (500个节点) 部署一个月后,网络停机时间减少了95%.
与历史基线相比,事件解决时间缩短了90%.
该框架展示了有效的预测异常检测和主动警报能力.

结论:

拟议的人工智能和物联网驱动的框架通过提供预测性,多租户意识的可见性,显著改善了MSP的网络监控.
边缘嵌入式AI推断管道为实时网络健康评估和故障预测提供了一种新的方法.
该系统提供了实质性的运营效益,包括减少停机时间和更快的事件解决,通过实时部署和客户反来验证.

关键词:

人工智能的人工智能是人工智能.分权化,去中心化.机器学习是机器学习.多租户的多租户模式网络监控网络监控网络监控

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