🚀 ElasticAI KubeWatch

ElasticAI KubeWatch is an AI-driven auto-scaling solution using Azure Functions and Azure Kubernetes Service (AKS) to dynamically adjust resources based on real-time application performance metrics. 📈

📋 Table of Contents

• Features
• Getting Started
• Documentation
• Deployment
• Configuration
• Contributing
• License

🎉 Features

• AI-powered auto-scaling of AKS clusters based on real-time performance metrics.
• Easily configurable scaling rules to handle varying workloads efficiently.
• Integrates with Azure Functions to enable dynamic resource adjustments.
• Includes machine learning model for demand forecasting.

🚀 Getting Started

To get started with ElasticAI KubeWatch, follow these steps:

1. Clone the repository: https://github.com/AnthonyByansi/ElasticAI_KubeWatch.git
2. Install the required dependencies: pip install -r requirements.txt
3. Configure the settings in config/config.yaml and config/scaling_rules.yaml.
4. Deploy the application to AKS using the provided scripts: ./scripts/deploy_to_aks.sh
5. Monitor the auto-scaling behavior through Azure Functions and AKS dashboard.

📖 Documentation

For detailed information on the architecture, deployment, and usage of ElasticAI KubeWatch, check out the Documentation folder:

• Architecture: Overview of the solution's design and components.
• Deployment: Step-by-step guide on deploying the application to AKS.
• User Guide: Instructions on configuring and using the auto-scaling solution.

🚀 Deployment

The ElasticAI KubeWatch solution can be deployed to Azure Kubernetes Service (AKS) using the provided deployment script:

./scripts/deploy_to_aks.sh

Make sure you have the necessary permissions and the AKS cluster is properly set up before running the script.

🔧 Configuration

ElasticAI KubeWatch provides configuration options through YAML files in the config directory:

• config.yaml: General settings for the application.
• scaling_rules.yaml: Rules for auto-scaling based on performance metrics.

👥 Contributing

Contributions to ElasticAI KubeWatch are welcome! To contribute, please follow our Contribution Guidelines.

📄 License

This project is licensed under the MIT License.