This repository contains the files for setting up a custom Software-Defined Networking (SDN) environment using Mininet for network emulation and the Floodlight Controller for intelligent routing, specifically implementing a K-shortest path finding capability.

The core functionality involves a custom Floodlight module that computes multiple optimized routes between any two switches in the network using Yen's algorithm, while also managing network partitioning into clusters and archipelagos.

The project is structured around two main components:

1. Network Emulation: A custom 8-switch, 8-host topology defined in Mininet (topology.py).
2. SDN Control Logic: A modified Floodlight controller module (TopologyInstance.java) that performs advanced topology discovery and path computation.

The Java component uses a highly optimized implementation of Dijkstra's algorithm for finding the shortest path (and broadcast trees) and extends this capability to Yen's algorithm for computing up to K-shortest paths (configurable) between any two nodes. An accompanying Python script (contoller.py) provides a local simulation of the shortest path using Dijkstra's algorithm for testing graph representations.

• Custom Topology: A dedicated 8-switch, 8-host (h1-h8 connected to s1-s8) network with specific inter-switch links for complex path testing.
• K-Shortest Path Routing (Yen's Algorithm): Implements an efficient method to find multiple paths between source and destination switches, which is critical for fault tolerance and load balancing in SDN.
• Optimized Shortest Path (Dijkstra's Algorithm): Used as the core subroutine in Yen's algorithm and for computing network broadcast trees.
• Dynamic Clustering: Uses a modified Tarjan's algorithm to identify strongly connected components (Clusters and Archipelagos) within the network, enhancing topology management.
• Simulated Pathfinding: An independent Python script demonstrates Dijkstra's pathfinding on the same network structure for visualization and testing.

You must have Mininet and a Floodlight controller (typically the source code for compilation) installed and configured on your system.

1. Clone the Repository:

   git clone [your-repository-url] cd Mininet-Floodlight-Pathfinding

2. Setup Mininet Topology: Copy the custom topology file into the directory where you will run Mininet (e.g., in your Mininet environment's main folder or a directory accessible by Mininet).

   cp topology.py /path/to/mininet/custom/topos/

3. Integrate Floodlight Module: The TopologyInstance.java file needs to be integrated into the Floodlight source code (specifically within the net.floodlightcontroller.topology package) and the Floodlight controller must be re-compiled and started.

   • Place the file: Place TopologyInstance.java into your Floodlight source tree: floodlight/src/main/java/net/floodlightcontroller/topology/TopologyInstance.java
   • Compile and Run Floodlight: Follow the standard Floodlight build process (e.g., using ant and then running the controller).

4. Install Python Dependencies: The accompanying Python script requires NumPy.

   pip install numpy

   (Note: CVXPY and Matplotlib badges are included as required, but are not strictly necessary for the current version of contoller.py.)

First, ensure your Floodlight controller is running. Then, execute the Mininet commands from commands.txt in your terminal:

# Clean up previous Mininet sessions sudo mn -c # Kill any existing process listening on the default OpenFlow port (6653) sudo fuser -k 6653/tcp # Start the custom topology 'mytopo' and connect it to the remote controller # (Floodlight should be running on the default IP/Port) sudo mn --custom topology.py --topo mytopo --controller remote

Mininet will start, and the switches (s1 through s8) will connect to the running Floodlight controller. The controller will then begin topology discovery and compute the path caches.

You can run the standalone controller.py script to simulate the shortest path calculation on the graph structure locally:

python contoller.py

Example Interaction: The script will prompt you for the start and target switch numbers (1 to 8):

Type start and target switches: 1 8 We found the following best path with a value of X. s1 -> s3 -> s8 

We welcome contributions to enhance this project, particularly to expand the pathfinding metrics in the Floodlight module or integrate the Python pathfinding output with the Mininet environment.

1. Report Issues: Use the GitHub Issues tracker to report bugs or suggest new features.
2. Feature Suggestions: Open an issue to discuss significant changes before starting development.
3. Pull Requests: Fork the repository, create a new branch, commit your changes, and submit a pull request. Please ensure your code follows the existing style and includes relevant documentation/comments.

This project is licensed under the MIT License.

For questions or support, please open an issue in this repository.