This project is a Python-based cloud-native application designed to monitor system resources such as CPU and memory usage. The application uses Flask for the backend and Plotly for creating interactive gauge visualizations. It is containerized using Docker and can be deployed to Kubernetes for scalable and resilient deployment.
- Real-Time Monitoring: Displays real-time CPU and memory utilization metrics.
- Interactive Gauges: Uses Plotly to visualize system metrics with interactive gauges.
- Dockerized: Built and packaged into a Docker container for easy deployment.
- Responsive Design: Utilizes Bootstrap for a modern, responsive user interface.
app.py: The Flask application that serves the monitoring data and the frontend.requirements.txt: Python dependencies required for the application.Dockerfile: Docker configuration for containerizing the application.index.html: The frontend HTML file with embedded Plotly visualizations..gitignore: Specifies files and directories to be ignored by Git.README.md: Project documentation.
- Python 3.9+
- Docker
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Clone the Repository
git clone https://github.com/<your-username>/cloud_monitoring.git cd cloud_monitoring
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Install Dependencies
pip install -r requirements.txt-
Run the Application Locally
python app.py
The application will be accessible at http://127.0.0.1:5001.
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Build and Run Docker Container
docker build -t monitoring-app .
docker run -p 5001:5001 monitoring-appAccess the application at http://127.0.0.1:5001.
To deploy this application on Kubernetes:
- Push Docker Image to a Registry
docker tag monitoring-app <your-registry>/monitoring-app:latest
docker push <your-registry>/monitoring-app:latest-
Create Kubernetes Deployment and Service YAML files Ensure you have Kubernetes configured and create the deployment and service YAML files to deploy the app.
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Apply the Configuration
kubectl apply -f deployment.yaml
kubectl apply -f service.yaml- Anomaly Detection: Implement ML models to detect anomalies in system metrics, such as unusual spikes in CPU or memory usage, which could indicate potential issues or security threats.
- Predictive Analytics: Use DL models to forecast future resource usage trends, helping to optimize resource allocation and prevent potential bottlenecks.
- Automated Scaling: Develop intelligent algorithms that automatically adjust the scaling of Kubernetes pods based on predicted resource usage patterns.
- Resource Utilization Forecasting: Integrate ML models to predict long-term trends in resource utilization, providing actionable insights for capacity planning.
- Advanced Graphs: Use DL techniques to enhance data visualization, creating more sophisticated and interactive visualizations that highlight patterns and insights.
- Personalized Dashboards: Use ML to create personalized dashboards based on user preferences and historical interactions, improving the relevance of displayed data.
- Natural Language Processing (NLP): Implement NLP capabilities to allow users to interact with the monitoring system through natural language queries, providing a more intuitive user experience.
- Cloud Provider Integration: Integrate ML and DL models with cloud-native monitoring solutions like AWS SageMaker, Google AI Platform, or Azure Machine Learning for enhanced analytics and scalability.
- Cross-Cluster Analytics: Extend ML capabilities to analyze and correlate metrics across multiple Kubernetes clusters, providing a comprehensive view of system performance and health.
- ML/DL Model Documentation: Provide detailed documentation on the ML and DL models used, including their training data, algorithms, and performance metrics.
- Community Contributions: Encourage contributions from the community to enhance ML/DL features, fostering collaboration and innovation.
These future enhancements aim to leverage the power of Machine Learning and Deep Learning to make the monitoring system more intelligent, predictive, and user-friendly. Your feedback and contributions towards these advancements are highly encouraged!