RouteNet is a Graph Neural Network (GNN) model that estimates per-source-destination performance metrics (e.g., delay, jitter, loss) in networks. Thanks to its GNN architecture that operates over graph-structured data, RouteNet revealed an unprecedented ability to learn and model the complex relationships among topology, routing and input traffic in networks. As a result, it was able to make performance predictions with similar accuracy than costly packet-level simulators even in network scenarios unseen during training. This provides network operators with a functional tool able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay, jitter, loss).
We strongly recommend use Python 3.7, since lower versions of Python may cause problems to define all the PATH environment variables. The following packages are required:
- Tensorflow >= 2.4. You can install it following the official Tensorflow 2 installation guide.
- NetworkX >= 2.5. You can install it using pip following the official NetworkX installation guide.
- Pandas >= 0.24. You can install it using pip following the official Pandas installation guide.
In order to test the installation, we provide a toy dataset that contains some random samples. You can simply verify your installation by executing the main.py file.
python main.py
You should see something like this:
4000/4000 [==============================] - 314s 76ms/step - loss: 67.8137 - MAPE: 67.8137 - val_loss: 150.7025 - val_MAPE: 150.7025
Epoch 00001: saving model to ../trained_modelsGNNetworkingChallenge\01-150.70-150.70
We provide two methods, one for training and evaluation and one for prediction. These methods can be found in the main.py file.
The main.py file automatically trains and evaluates your model. This script trains during a max number of epochs, evaluating and saving the model for each one.
See LICENSE for full of the license text.
Copyright Copyright 2021 Universitat Politècnica de Catalunya
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.