Self-Driving Network and Service Coordination Using Deep Reinforcement Learning
Using DDPG for coordinating online scaling, placement, and scheduling of services and rapidly incoming requests. Services consist of chained components that need to be instantiated at nodes in the substrate network and that incoming requests need to traverse in a predefined order. Our approach learns how to do this by itself just from experience, maximizing the amount of successfully served requests and minimizing end-to-end delay. It works with realistically available monitoring information, containing partial and delayed observations of the network.
Citation
If you use this code, please cite our paper:
@inproceedings{schneider2020selfdriving,
title={Self-Driving Network and Service Coordination Using Deep Reinforcement Learning},
author={Schneider, Stefan and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur},
booktitle={International Conference on Network and Service Management (CNSM)},
year={2020},
publisher={IFIP/IEEE}
}
Best Student Paper at IEEE/IFIP CNSM 2020
Setup
You need to have Python 3.6+ and venv module installed.
Create a venv
On your local machine:
# create venv once
python3.6 -m venv ./venv
# activate the venv (always)
source venv/bin/activate
# update setuptools
pip install -U setuptools
Install dependencies
pip install -r requirements.txt
This also installs the required coord-sim simulator and common-utils package.
Use the RL agent
All options:
$ rlsp -h
Usage: rlsp [OPTIONS] AGENT_CONFIG NETWORK SERVICE SIM_CONFIG STEPS
rlsp cli for learning and testing
Options:
--seed INTEGER Specify the random seed for the environment and
the learning agent.
-t, --test TEXT Name of the training run whose weights should
be used for testing.
-w, --weights TEXT Continue training with the specified weights
(similar to testing)
-a, --append-test Append a test run of the previously trained
agent.
-v, --verbose Set console logger level to debug. (Default is
INFO)
-b, --best Test the best of the trained agents so far.
-e, --test-episodes INTEGER Set the number of testing episodes
-ss, --sim-seed INTEGER Set the simulator seed
-gs, --gen-scenario PATH Diff. sim config file for additional scenario
test
-h, --help Show this message and exit.
Training and testing
Example for short training then testing:
rlsp res/config/agent/sample_agent.yaml res/networks/sample_network.graphml res/service_functions/abc.yaml res/config/simulator/sample_config.yaml 1000 --append-test
Results are stored under results/
according to the input arguments and the current time stamp.
There, you'll find copies of the used inputs, the trained weights, logs, and all result files of any test runs that you performed with these weights.
Testing
To run another test run with the trained weights, specify the <timestamp_seed>
of the training run. For testing, it is recommended to use 200 steps as it is the duration of one episode and use -e
to specify the number of testing episodes.
For example:
rlsp res/config/agent/sample_agent.yaml res/networks/sample_network.graphml res/service_functions/abc.yaml res/config/simulator/sample_config.yaml 200 -t <timestamp_seed> -e 1
Testing with a different simulator configuration (Generalization)
To train an agent and test it on multiple scenarios (simulator configurations), use the -gs
to specify a different simulator config file to test in combination with --append-test
.
Example for testing with generalization:
rlsp res/config/agent/sample_agent.yaml res/networks/sample_network.graphml res/service_functions/abc.yaml res/config/simulator/sample_config.yaml 1000 --append-test -gs res/config/simulator/sample_config.yaml
Learning Curves using Tensorboard
To view the learning curve of all agents, i.e., the episode reward over time, use tensorboard
:
tensorboard --logdir==./graph
You can also filter to only show curves of a specific agent config, network (and service and config) by setting the --logdir
correspondingly:
tensorboard --logdir==./graph/<agent_config>/<network>/<service>/<simulator_config>
Visualizing/Analyzing Results
To get a better understanding of what the agent is doing, there is an Juypter notebook example_eval.ipynb
.
It's just an example; you won't be able to run it without all the results (which are too large for the repo).
To create a similar notebook for evaluation:
# first time installation
pip install -r eval_requirements.txt
# run jupyter server
jupyter lab
Training and testing on multiple scenarios
There is script provided in the scripts
folder that utilizes the GNU Parallel utility to run multiple agents at the same time to speed up the training process.
./scripts/run_parallel.sh
To run long training sessions in remote environments without risking to stop the sessions due to possible connectivity issues, it is recommended to run the experiments with the screen
linux tool.
- For that, start a new
screen
withscreen -S rl-parallel
. - Configure the agent and the network, service, config files in the
scripts
directory to match the scenarios that you want to run. Here, the lines of network, service, config are matched by lines (not all permutations), eg, 1. network is matched with 1. service and 1. config. Then all seeds are used for all scenarios. - Inside the screen, with the venv activated, run
./scripts/run_parallel.sh
from the project root.
Acknowledgement
This project has received funding from German Federal Ministry of Education and Research (BMBF) through Software Campus grant 01IS17046 (RealVNF).