DataCenter AI Monitor

License: MIT n8n OpenAI PostgreSQL Python Streamlit Docker

An autonomous infrastructure health-monitoring and diagnostic system powered by AI Agents and n8n.

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🚀 The Business Problem

• Situation: Modern Data Centers generate millions of telemetry points, leading to extreme alert fatigue. Critical infrastructure failures were often buried under thousands of low-priority logs, delaying response times and increasing operational risks.
• Task: To architect an autonomous system capable of filtering telemetry noise, performing real-time Root Cause Analysis (RCA), and dispatching actionable intelligence to on-site engineers—all while ensuring the environment is portable and scalable.
• Action: I orchestrated a multi-layered AIOps solution containerized with Docker for seamless deployment. I used n8n as the central engine, integrated PostgreSQL for time-series data, and leveraged OpenAI's GPT-4o to serve as an "AI Diagnostic Engineer" that contextually analyzes alerts and generates remediation plans.
• Result: Developed a production-ready framework that reduces initial diagnostic time from minutes to seconds, providing high-fidelity incident reports with 100% automated RCA coverage for critical failures.

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🏗️ System Architecture

The system operates in three distinct layers to ensure reliability and intelligence:

1. Ingestion Layer: Real-time metrics (CPU, Temp, Disk, UPS) are streamed into a PostgreSQL database.
2. Intelligence Layer (n8n + AI):
   • Monitor Workflow: Polls for critical thresholds and triggers the AI Agent.
   • AI Diagnostic: GPT-4o receives the full context of the alert and performs a Root Cause Analysis (RCA).
3. Action Layer: Results are pushed to an Executive Dashboard and dispatched via high-priority Gmail alerts with actionable remediation steps.

graph TD
    A[Telemetry Sources] -->|Streaming| B[(PostgreSQL DB)]
    B -->|Check Thresholds| C{n8n Orchestrator}
    C -->|Query Context| B
    C -->|RCA Request| D[OpenAI GPT-4o Agent]
    D -->|Analysis & Steps| C
    C -->|Store Incident| B
    C -->|Alert| E[Email/Slack Notification]
    B -->|Live Feed| F[Streamlit Dashboard]

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⚙️ How it Works: Workflow Deep Dive

1. The Monitor & Analyzer (01-monitor.json)

This is the heart of the system. Every 5 minutes, n8n queries the database for any device reporting a critical status.

• The Prompt: The AI isn't just chatting; it's primed with a specific persona: Senior Data Center Engineer.
• The Output: It generates a structured 3-point report: Root Cause, Business Impact, and a 3-step Remediation Plan.

2. The Alert Dispatcher (02-Gmail-Alert-Dispatcher.json)

Once an incident is analyzed, this workflow ensures the right people know immediately.

• Smart Filtering: Only alerts with a severity of 4/5 or higher trigger the emergency email.
• Data Sanitization: Uses custom JavaScript nodes to clean the AI output, formatting it into a beautiful, readable HTML email for mobile and desktop.

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🖥️ Executive Dashboard

System Demo

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🚀 Despliegue Local

Inicio Rápido (3 pasos)

1. Configura tu API Key de OpenAI

   # Edita el archivo .env
   OPENAI_API_KEY=sk-tu-clave-aqui
   

2. Inicia el proyecto

   ./start.sh
   

3. Accede a n8n: http://localhost:5678 (admin / admin123)

URLs de Acceso

Servicio	URL	Credenciales
n8n	http://localhost:5678	admin / admin123
Dashboard	http://localhost:8501	-
PostgreSQL	localhost:5432	datacenter_user / datacenter_pass_2024

📖 Documentación completa: DEPLOYMENT.md | QUICKSTART.md

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🛠️ Technology Stack

• Orchestration: n8n (Low-code workflow automation)
• Infrastructure: Docker (Containerization for portable deployment)
• Artificial Intelligence: OpenAI GPT-4o (LLMs for RCA)
• Database: PostgreSQL (Structured incident logging)
• Frontend: Streamlit (Real-time monitoring UI)
• Scripting: Python 3.11 & JavaScript (Node.js)

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📩 Contact & Collaboration

• LinkedIn: daniel-garcía-belman-99a298aa
• Portfolio: danieljcvv-portfolio.vercel.app
• Email: [email protected]

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Developed by Daniel-jcVv | Powered by n8n, OpenAI & PostgreSQL

Soli Deo Gloria.