Framework for ns-3

2023 - 2025

Workflow of simulation with ns-3

Developed a framework to automate and standardize the simulation workflow from planning to plotting, enabling researchers to focus on core strategic planning and data analysis.


⚙️ Statistical Framework for System Level Simulator (ns-3)


Concept of ns-3 Statistical Framework

Developed a statistical framework for the ns-3 simulator to facilitate the collection and analysis of simulation results.

Developed a containerized statistical framework that automates the entire pipeline from parameter input to result analysis using Django and Apache Airflow. I implemented a robust data architecture using PostgreSQL and MongoDB to ensure data integrity and scalability. By leveraging Docker for the entire ecosystem, I achieved environment consistency and streamlined the deployment process.

  • Automated Workflow: Replaced manual simulation execution with Airflow DAGs, reducing human error.

  • Hybrid Database Strategy: Optimized data storage by using PostgreSQL for structured logs and MongoDB for large-scale simulation results.

  • Scalable Architecture: Ensured seamless scalability and environment parity across different systems via Docker containers.

Category Technologies Used
Backend & Orchestration Django, Apache Airflow, Python
Database PostgreSQL (Relational), MongoDB (NoSQL)
Infrastructure Docker, Docker-compose
Tools & Monitoring MongoDB Compass, Microsoft Teams Integration


⚙️ Deep Learning Framework for System Level Simulator (ns-3)


Architecture of MLflow Tracking Server with PostgreSQL and Local File System

Developed an advanced deep learning framework by integrating MLflow into the existing statistical architecture to automate experiment tracking and model management.

I established a robust pipeline between localhost and remote hosts to log hyperparameters, metrics, and artifacts in real-time. This enhancement significantly improved reproducibility and research efficiency for complex system-level simulation-based AI studies.

  • Enhanced Experiment Traceability: Leveraged MLflow Tracking Server to monitor all DL training sessions and hyperparameter tuning.

  • Scalable Artifact Management: Implemented an Artifact Proxy to securely store and retrieve large-scale model files and configurations across remote hosts.

  • Integrated Data Pipeline: Combined PostgreSQL for metadata tracking and local/remote file systems for comprehensive storage.