reinforcement-learning-for-per-flow-buffer-sizing

Contact: Maximilian Bachl

This repository contains the code for the paper LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning.

The code was run on Debian 10 (buster) on Linux kernel 4.19.0-9-amd64 with g++ 8.3.0.

Native installation (not recommended, Docker installation is easier)

Downloading libtorch

Download the C++ version of libtorch for CPUs. If you want to download a newer version, make sure that it is the Pre-cxx11 ABI version. Otherwise you'll have to change some flags in the build process.
Unpack it and move the contained directory libtorch to the root directory of this repository (the same directory in which there's the ns-allinone-3.30.1 directory)

Compiling

cd ns-allinone-3.30.1/ns-3.30.1/
./waf clean
./waf configure
./waf build

Docker installation

Downloading libtorch (same as for the native installation)

Download the C++ version of libtorch for CPUs. If you want to download a newer version, make sure that it is the Pre-cxx11 ABI version. Otherwise you'll have to change some flags in the build process.
Unpack it and move the contained directory libtorch to the root directory of this repository (the same directory in which there's the ns-allinone-3.30.1 directory)

Using docker to run it

cd docker
docker build -f Dockerfile . -t rl_image
docker run -v $(realpath -s ..):/repo -w /repo/ns-allinone-3.30.1/ns-3.30.1/ --entrypoint bash rl_image run_all.sh
docker run -v $(realpath -s ..):/repo -w /repo/ns-allinone-3.30.1/ns-3.30.1/ --entrypoint bash -it rl_image
Now you're in the Docker image and all commands below for training or evaluating should work as usual.

Training an RL model

If the build finished successfully (this can take some time), you can train a model.

Assuming you're in the ns-3.30.1 directory, run

OMP_NUM_THREADS=1 LD_LIBRARY_PATH=../../libtorch/lib:$LD_LIBRARY_PATH ./waf -v --run "examples/traffic-control/rl"

To change parameters like the number of cpus to use for training, the tradeoff parameter alpha etc., change the variables on top of the file ns-allinone-3.30.1/ns-3.30.1/examples/traffic-control/rl.cc and recompile.

Evaluating

Evaluating an RL model

To evaluate a set of trained weights run

OMP_NUM_THREADS=1 LD_LIBRARY_PATH=../../libtorch/lib:$LD_LIBRARY_PATH ./waf -v --run "examples/traffic-control/rl <path_to_weights>"

Evaluating other AQM mechanisms

To evaluate, for example, FqCoDel, run

    OMP_NUM_THREADS=1 LD_LIBRARY_PATH=../../libtorch/lib:$LD_LIBRARY_PATH ./waf -v --run "examples/traffic-control/rl FqCoDelQueueDisc"

To run Fifo with a maximum queue size of 100, run

    OMP_NUM_THREADS=1 LD_LIBRARY_PATH=../../libtorch/lib:$LD_LIBRARY_PATH ./waf -v --run "examples/traffic-control/rl FifoQueueDisc 100"

Plotting

Plotting training runs

During training, various values are logged.

To plot them, run the following command:

./plot_metrics.py results/RLQueueDisc/logs/<name_of_the_file_to_be_plotted>

Plotting evaluation results

The following commands are supposed to be run after an AQM mechanism was evaluated.

To plot, for example, all queue traces produced by an evaluation of Fifo with queue size 1000, run the following command:

ls -d $PWD/results/FifoQueueDisc/queueTraces/1000/*.plotme | xargs ./plot_something.py

To plot the behavior of an AQM mechanism and get performance metrics, run, for example, the following command:

./plot_df.py results/RLQueueDisc/logs/*

Pre-trained weights

All weights that are used to generate the plots in the paper can be found in the directory reinforcement-learning-for-per-flow-buffer-sizing/ns-allinone-3.30.1/ns-3.30.1/results/RLQueueDisc/weights.

CN-TU/reinforcement-learning-for-per-flow-buffer-sizing