I’m working with an OMNeT++ simulator version 4.6 that uses the INET framework for network simulation. My goal is to establish real-time communication between the simulator and a Python machine learning algorithm.
The simulator needs to continuously send network metrics like signal-to-noise ratio measurements and mobile device coordinates to the Python ML module. The Python side processes this information to train a model that optimizes network performance.
Once the Python algorithm calculates the optimal network adjustments, it must send these control commands back to the OMNeT++ simulator. This needs to happen while both applications are running simultaneously.
What’s the best approach to implement this bidirectional communication between these two separate processes? I need a solution that works during simulation execution without stopping either program.
redis works gr8 for this. just set up a redis server between omnet++ and python - your sim pushes metrics to redis channels while python subscribes to them. for control commands, python sends them back and omnet++ picks em up. redis handles buffering automatically and you get persistence if either process crashes. way easier than rolling your own sync, plus pub/sub is perfec for real-time data.
Shared memory works great for high-frequency data exchange - I’ve had good luck with it. Use POSIX shared memory on Linux or memory-mapped files on Windows. The performance gains are huge with continuous metric streams since you skip all the serialization overhead that comes with sockets or pipes. For OMNeT++, just allocate a shared memory segment and write your network data straight to structured buffers. Python can hit the same memory region through the mmap module. You’ll need proper synchronization with semaphores or mutexes to avoid race conditions. Debugging gets messier compared to sockets, but the latency improvements are worth it for real-time stuff. I usually set up one memory block for sensor data going to Python and another for control commands coming back to OMNeT++.
I’ve hit similar issues with inter-process communication. TCP sockets between OMNeT++ and Python work best in my experience.
Create a custom OMNeT++ module that opens a socket to your Python script. Use the built-in socket libraries to send network metrics as JSON or binary. Run a socket server in Python that listens for the simulator data.
For bidirectional communication, I use a simple request-response pattern. OMNeT++ sends metrics, waits for the ML response, then applies control commands. ZeroMQ works well if you need more complex communication.
Timing is crucial - don’t let OMNeT++ block waiting for Python responses. Use non-blocking sockets or separate threads for communication.
This presentation covers Python integration with OMNeT++ and has good implementation details:
I’ve used this setup in production for real-time data exchange between C++ simulators and Python analytics. Socket approach gives you solid control over data flow and timing.
Named pipes work great for this setup. They’ve got less overhead than sockets and are way simpler to implement when both processes are on the same machine. In OMNeT++, just create a module that writes to a named pipe using standard file operations. Your Python script reads from it continuously. For the reverse direction, use a second pipe - Python writes control commands, OMNeT++ reads them. The tricky part is syncing simulation time. I’d send timestamp info with each data packet so Python knows where it is in the simulation timeline. Also throw in a heartbeat mechanism to catch when either process dies. For serialization, go with Protocol Buffers over JSON. They handle binary data better and give you proper type safety, which matters when you’re dealing with tons of coordinate and SNR data. One thing that bit me - make sure you flush the pipe buffers after each write. Otherwise data just sits there and screws up timing in your ML training loop.
