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DASH - Distributed Adaptive Serverless Hosting

What is DASH?

DASH is a peer-to-peer distributed system for executing serverless functions and deploying tasks efficiently. It leverages Tauri + Nextjs to create a robust platform where clients can submit code, schedule tasks, and execute them seamlessly across a network of connected nodes. DASH redefines task scheduling and execution by introducing an AI-powered DASH Scheduler , which dynamically assigns tasks based on node availability and resource utilization.

Key Features

  • Peer-to-Peer Execution: Facilitates distributed execution of serverless functions across multiple nodes.
  • AI-Based Task Scheduling: DASH Scheduler intelligently allocates tasks for optimal performance and efficiency.
  • Real-Time Task Management: Uses Firebase for instant updates and communication.
  • Scalable and Flexible: Easily scales with the addition of new nodes.
  • Secure Execution: Ensures the security and integrity of code and results throughout the pipeline.

Screenshots

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image

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How It Works

1. # Clients Submit Code/Docker Image:
   - Updated in database with metadata and marked as 'pending.'
2. # DASH Scheduler:

   - The Scheduler listens for pending tasks, evaluates resource availability, and assigns tasks to the most suitable nodes.

3. # Task Execution:

   - Idle clients receive task assignments, execute the code, and report the results.

4. # Result Reporting:

   - Execution results are fetched and instantly updated for the original client.

5. # Client Notifications:
   - The system notifies clients with detailed results and execution logs.

Technical Details

Frontend

  • Framework: Tauri + Next.js
  • Styling: Tailwind CSS
  • Tauri API: Used for secure desktop interactions.

Backend

  • Database: Firebase Realtime Database and Firestore. (for the prototype)
  • Deployment: Scheduler is deployed in DASH itself

Task Scheduler

  • Language: Python
  • Libraries: Firebase Admin SDK
  • Task Distribution: Custom algorithm for dynamic task allocation.

Contributors

Adithya Krishnan
Adithya Krishnan 		Ferwin Lopez
Ferwin Lopez 		Nevia Sebastian
Nevia Sebastian 		Nikita Nair
Nikita Nair

License

This project is licensed under the MIT License.