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AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular

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Summary
This summary is machine-generated.

This study introduces an AI and IoT framework for network monitoring, enhancing predictive anomaly detection for Managed Service Providers (MSPs) and reducing downtime. The system significantly cuts incident resolution times and operational costs.

Keywords:
artificial intelligencedecentralisationmachine learningmulti-tenantnetwork monitoring

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Managed Service Providers (MSPs), particularly small- and medium-sized enterprises (SMEs), struggle with predictive network visibility across diverse client infrastructures.
  • Current network monitoring solutions often lack integrated predictive analytics and edge intelligence capabilities.
  • Achieving multi-tenant-aware monitoring remains a significant challenge for MSPs managing distributed client networks.

Purpose of the Study:

  • To develop and validate an AI- and IoT-driven framework for predictive network monitoring.
  • To enhance visibility and anomaly detection for MSPs managing multi-tenant environments.
  • To reduce network downtime and incident resolution times through proactive alerting and edge intelligence.

Main Methods:

  • Integration of edge IoT nodes (Raspberry Pi Prometheus modules) with machine learning models for data collection and analysis.
  • Utilization of Prometheus, Grafana, and Mimir for data management, visualization, and storage.
  • Implementation of Simple Linear Regression (SLR), K-Means clustering, and Long Short-Term Memory (LSTM) models for predictive anomaly detection and fault classification.
  • Containerized deployment of AI modules at the edge or centrally, with risk metrics fed back into Prometheus.
  • Secure tenant isolation using VPN tunnels and token-based authentication, ensuring GDPR compliance.

Main Results:

  • A one-month deployment across five MSP clients (500 nodes) showed a 95% reduction in network downtime.
  • Incident resolution time was reduced by 90% compared to historical baselines.
  • The framework demonstrated effective predictive anomaly detection and proactive alerting capabilities.

Conclusions:

  • The proposed AI- and IoT-driven framework significantly improves network monitoring for MSPs by providing predictive, multi-tenant-aware visibility.
  • Edge-embedded AI inference pipeline offers a novel approach to real-time network health assessment and fault prediction.
  • The system delivers substantial operational benefits, including reduced downtime and faster incident resolution, validated by live deployment and client feedback.