Inspiration
We were constantly frustrated by the limitations of conventional file transfer protocols. Current systems suffer from:
Slow speeds when network conditions fluctuate or latency increases.
Dropped transfers that require manual restarts, leading to lost time and productivity.
A lack of intelligence to automatically select the optimal path or protocol (e.g., choosing between FTP, SFTP, or a custom protocol) based on file type, size, and network state.
Our inspiration was to build a transfer system that could think, heal, and self-optimize to deliver data with guaranteed speed and reliability, regardless of network hurdles
Challenges we faced
Real-Time Data Collection: Gathering and feeding accurate, real-time network performance data to our ML model was the biggest hurdle. We overcame this by building a dedicated, low-latency monitoring service.
Protocol Interoperability: Ensuring our "smart" protocol could seamlessly fall back to industry standards like SFTP or HTTPS when necessary required careful design and rigorous testing.
Cross-Platform Deployment: Getting the system to run reliably on both Windows and Linux environments introduced unexpected challenges related to file path handling and networking APIs.
How we built it
Our Smart File Transfer System incorporates several key components:
AI/ML Path Optimizer: We implemented a lightweight machine learning model trained on network latency, throughput, and error rates. This model predicts the best transfer protocol and path in real-time before initiating the transfer.
Adaptive Chunking: The system dynamically adjusts the size of the data chunks being sent. In a stable network, we use large chunks for efficiency; in a shaky network, we reduce chunk size to minimize data loss risk.
Self-Healing Protocol: A proprietary protocol layer was developed to detect dropped connections instantly and resume the transfer from the exact point of failure without manual intervention, ensuring 100% data integrity.
Security Layer: All data is encrypted using [Mention your encryption method, e.g., AES-256] end-to-end, and all transfers require [Mention authentication, e.g., two-factor authentication].
What we learned
This hackathon was a steep learning curve in distributed systems and network programming. Specifically, we learned:
The complexity of implementing reliable connection state management and checkpointing for seamless transfer resumption.
The challenges of deploying and training a real-time ML model for network prediction under strict resource constraints.
Mastering the intricacies of socket programming and protocol selection to minimize overhead.
Built With
- amazon-web-services
- cryptography
- custom-tcp/udp
- docker
- git
- python
- scikit-learn-(for-ml-path-optimizer)
- socket-api
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