AI-Based Cloud Resource Auto-Scheduler

Jai Tiwari

Completed work

Cloud Infrastructure Architect

AI Developer

Researcher

AWS

Flask

TensorFlow

Electronics

💙 Theme Color: Bluish

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

ai-cloud-auto-scheduler/
│
├── app.py
├── train_model.py
├── autoscaler.py
├── config.yaml
├── requirements.txt
├── data/
│   └── historical_data.csv
├── model/
│   └── prediction_model.h5
└── README.md

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 )

Dataset

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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Completed work

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