Subject: [nmrg] Graph Neural Networking challenge 2020

From: José Suárez-Varela <[email-masked]>
Date: Fri, 19 Jun 2020 19:52:07 +0200

Dear Colleagues,

The Barcelona Neural Networking center (BNN-UPC) is glad to announce the 
"*Graph Neural Networking challenge 2020*", the first competition in the 
world on Graph Neural Networks applied to computer networks. This 
challenge is organized as part of the "ITU AI/ML in 5G Challenge".

Registration is now open and free of charge for all participants 
(*Deadline Jun 30th*).

Website: https://bnn.upc.edu/challenge2020

Registration: https://challenge2020.bnn.upc.edu/registration 
<https://www.google.com/url?q=https%3A%2F%2Fchallenge2020.bnn.upc.edu%2Fregistration&sa=D&sntz=1&usg=AFQjCNG3IFq9dGNjmwgw9RzLUmhyw5nk3A>

If you have any questions or comments, please do not hesitate to contact 
us at: [email-masked]

=====================================================

[Overview]

In recent years, Graph Neural Networks (GNN) have shown a strong 
potential to be integrated into commercial AI products for network 
control and management. Early works using GNN have demonstrated an 
unprecedented capability to learn from different network characteristics 
that are fundamentally represented as graphs, such as the topology, the 
routing configuration, or the traffic that flows along a series of nodes 
in the network. In contrast to previous solutions based on Machine 
Learning (ML), GNN enables to produce accurate performance predictions 
even in networks unseen during the training phase. This enables to train 
a GNN product in a controlled testbed ( e.g., in a networking lab) and 
then being able to deploy it directly in real-world networks.

Nowadays, GNN is a hot topic in the ML field and, as such, we are 
witnessing significant efforts to leverage its potential in many 
different fields (e.g., chemistry, physics, social networks). In the 
networking field, the application of GNN is gaining increasing attention 
and, as it becomes more mature, is expected to have a major impact in 
the networking industry.

[Challenge description]

The goal of this challenge is to create a solution based on neural 
networks that estimates performance metrics given a network snapshot. 
More in detail, this solution must predict the resulting 
per-source-destination mean per-packet delay given: (i) a network 
topology, (ii) a network configuration (routing, queue scheduling), and 
(iii) a source-destination traffic matrix.

We provide as baseline RouteNet, a GNN architecture recently proposed to 
estimate per-source-destination performance metrics (e.g., delay, 
jitter, loss) in networks. However, RouteNet does not include support 
for modeling the impact of queue scheduling on network performance and, 
consequently, it produces poor estimates in the network scenarios 
considered in this challenge.

Participants are encouraged to update RouteNet or submit their own 
neural network architectures.

[Main resources]

- Baseline GNN model: Open source implementation of RouteNet, including 
a tutorial on how to use it and modify fundamental characteristics of 
the model 
(https://github.com/knowledgedefinednetworking/RouteNet-challenge).
- Training/validation datasets
- Python API to easily read data from the datasets provided
- Mailing list for participants and people interested (Link to 
subscribe: 
https://mail.knowledgedefinednetworking.org/cgi-bin/mailman/listinfo/challenge-kdn). 

[Timeline]

* Registration open to participants: *Deadline Jun 30th*
* Evaluation phase: Sep 11th- Sep 25th
* Winners (top 3) official announcement: Oct 21st
* Awards and presentation: Nov-Dec 2020

Best Regards,

BNN team

------------------------
Barcelona Neural Networking center
Universitat Politècnica de Catalunya

Contact email: [email-masked]