AI-Based Cloud Resource Auto-Scheduler
Jai Tiwari
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AI-Based Cloud Resource Auto-Scheduler
Overview
The AI-Based Cloud Resource Auto-Scheduler is an intelligent system that automatically scales cloud resources up or down based on predicted demand. Using AI-driven algorithms, it analyzes historical data and real-time user activity to make precise predictions, ensuring optimal resource allocation and minimizing costs and idle time.
This project enables businesses to efficiently manage their cloud infrastructure, prevent resource over-provisioning or under-provisioning, and save on cloud costs through dynamic, automated scaling.
Features
Demand Prediction: Predicts cloud resource demand using AI models based on historical and real-time user data.
Auto-Scaling: Automatically adjusts cloud resources to meet predicted demand.
Cost Optimization: Minimizes cloud infrastructure costs by preventing unnecessary resource allocation.
Real-Time Monitoring: Continuously tracks system performance and adjusts scaling in real-time.
Cloud Provider Integration: Supports integration with major cloud platforms like AWS, Google Cloud, and Microsoft Azure.
Architecture
The system is composed of the following key components:
Data Collector: Gathers historical usage data and real-time user activity.
AI Prediction Engine: Utilizes machine learning algorithms (e.g., time-series forecasting, reinforcement learning) to predict future resource needs.
Cloud Resource Manager: Interfaces with cloud providers to automatically scale resources.
Monitoring Module: Tracks system performance and provides feedback for improving prediction accuracy.
Project Structure
Prerequisites
Python 3.8+ ( For Backend and AI Models )
HTML, CSS, Bootstrap, JS ( for Web Interface )
Cloud account with AWS, GCP, or Azure (with autoscaling enabled)
Libraries:
TensorFlow/PyTorch for AI model
Pandas, NumPy (for data processing)
Boto3 (for AWS) or equivalent for cloud integration
Flask (for web hosting & backend linkage)
SQLAlchemy ( for backend database storage )
Overall Tech Stack
Installation
Clone the repository:
Install dependencies:
Configure cloud provider credentials:
For AWS: Set up your AWS credentials in
~/.aws/credentials.For GCP/Azure: Follow the respective setup guides to integrate API keys.
Run the project:
Usage
Collect Data: Ensure that historical usage data and real-time user activity are being fed into the system.
Train Model: Use the provided script to train the AI model on historical data:
Auto-Scaling: Deploy the auto-scaling service and let it handle resource management based on AI predictions. Configuration
Scaling thresholds: Define thresholds for resource scaling in config.yaml.
Cloud Provider API: Update API configurations in the config.yaml file based on your cloud provider.
Future Improvements
Support for multi-cloud integration (AWS, GCP, Azure).
Enhanced prediction models incorporating additional metrics like network traffic and storage demand.
Predictive maintenance to auto-detect resource failures.
Contributing Contributions are welcome! Please open an issue or submit a pull request for any bug fixes or new features.
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License. See the LICENSE file for details.
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Posted Mar 31, 2025
The AI-Based Cloud Resource Auto-Scheduler is an intelligent system that automatically scales cloud resources up or down based on predicted demand. Using AI-dr…
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Timeline
Oct 21, 2024 - Oct 22, 2024
