25 Sep
2019
25 Sep
'19
10:14 a.m.
Thank you Jose. I have read the paper (many times :-)). I have seen the details of the
evaluation with the Geant2 network, but there is no mention of the GBN network in the
paper.
I am perfectly comfortable with processing the data (you can see my code here:
https://github.com/Data-Science-Projects/demo-routenet).
Specifically for the GBN network, I wanted to see what the topology looks like. I have the
NED file, but I can’t use that NED file with OMNet (for reasons discussed elsewhere).
I can, of course, manually reverse engineer the NED file. But I wanted to ask if there was
already a topology diagram just to save me the effort.
Regards
Nathan
On 25 Sep 2019, at 11:07, Jose Suárez-Varela
<jsuarezv(a)ac.upc.edu> wrote:
Hello Nathan,
All these datasets where used in our paper:
Krzysztof Rusek, José Suárez-Varela, Albert Mestres, Pere Barlet-Ros, Albert
Cabellos-Aparicio; "Unveiling the potential of Graph Neural Networks for network
modeling and optimization in SDN," in Proceedings of ACM Symposium on SDN Research
(SOSR) , pp. 140-151, April 2019.
Particularly, we trained RouteNet only with samples of the NSFNET dataset to predict the
delay and jitter. Then, we evaluate the accuracy of the models already trained. This
evaluation is made separately on the three datasets (NSFNET, GBN and GEANT2) to test the
generalization capability of the model.
Please, find more details in Section 4 (Evaluation of the accuracy of the GNN model) of
the paper.
Also, you can find information on how to process the datasets at the following link:
http://knowledgedefinednetworking.org/data/README_gnn.pdf
Regards,
José
El 22/09/2019 a las 16:55, Nathan Sowatskey escribió:
> Hi
>
> On this page:
>
>
https://github.com/knowledgedefinednetworking/Unveiling-the-potential-of-GN…
>
> I have seen that there is this data set:
>
> http://knowledgedefinednetworking.org/data/GBN.zip
>
> It is described as having been used for evaluation, but I can’t find anything else
that refers to it.
>
> Can anyone tell me more please?
>
> Many thanks
>
> Nathan
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