Cloud Resource Scheduler

A lightweight automation tool to start, stop, and manage cloud resources (AWS, Azure, GCP) on custom schedules.
It is designed to reduce costs, optimize resource usage, and provide fine-grained control with features like scheduling, manual triggers, and toggling.
The application also analyzes cloud usage, detects idle or underutilized resources, and generates recommendations along with cost and utilization graphs.

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Features

• Scheduled Actions – Automatically start/stop resources based on custom time rules.
• Toggle Rules – Enable or disable automation rules without deleting them.
• Instant Triggers – Run rules immediately on demand.
• Multi-Cloud Support – Works with AWS, Azure, and GCP resources.
• Resource Analysis – Detects idle/underutilized resources and provides recommendations.
• Cost & Usage Insights – Generates cost breakdowns, utilization graphs, and monitoring dashboards.
• Safe & Controlled – Prevents accidental deletion while allowing temporary overrides.

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Tech Stack

• Language: Python
• Framework: Streamlit (Dashboard)
• Database: PostgreSQL
• Cloud SDKs: AWS SDK (boto3), Azure SDK, GCP SDK
• Deployment: Docker, Kubernetes (optional)

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Usage

• Launch the dashboard with streamlit run app/dashboard.py.
• Connect your AWS/Azure.
• Create a Rule – Define schedule, action (start/stop), and target resource.
• Toggle Rule – Enable/disable rules anytime.
• Trigger Now – Execute instantly when needed.
• Monitor – View cost graphs, idle resources, and recommendations.

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Mock vs Real Data

• This project supports both mock data and real resource data:

• In aws_service.py and azure_service.py, set the flag:

  • True → Use mock data (ensures graphs and dashboards are always populated).
  • False → Use real resource data (requires active AWS/Azure credentials).

• This allows anyone to run the project locally without needing active cloud resources, while still being able to switch to real data when available.

• The screenshots below demonstrate real AWS data as proof, while mock mode ensures a consistent demo experience.

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Screenshots

1. Resouces on AWS

2. Idle Resources

3. Cost Dashboard

4. Cost Graph

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Getting Started

Prerequisites

• Python (>= 3.9)
• pip (Python package manager)
• Docker (optional, for containerized deployment)
• Cloud credentials (AWS, Azure)

Installation

# Clone repo
git clone https://github.com/singhsayan/cloud-resource-scheduler.git
cd cloud-resource-scheduler

# Create virtual environment
python3 -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run application
streamlit run app/dashboard.py