Dear Pro J. Suárez-Varela, S. Carol-Bosch, K. Rusek, P. Almasan, M. Arias, P. Barlet-Ros, A. Cabellos-Aparicio:
Hello Pro J. Suárez-Varela, S. Carol-Bosch, K. Rusek, P. Almasan, M. Arias, P. Barlet-Ros, A. Cabellos-Aparicio, I am sorry to disturb you in your busy schedule. I'am a student of Guilin University of Electronic Science and Technology from China.
Recently, Iam learning how to do route optimization with grahp neural networks. Your paper RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN gives me great inspiration. I ran the model code after reading your paper, but I can't see how much about the model regarding the input and output dimensions, for example, the shape of link_inputs=(? ,? ,32). I would like to ask about this? What is the size of the How can I can see it in the code?
No matter whether you help me or not, best wishes to you. I would appreciate it if you could help me.
Thanks and regards.
Sincerely,
Xiaolan, Xie
2023/2/19
Hi,I am a student from China,and I read your paper "Extending RouteNet to model more complex networking scenarios" recently, which introduces an extended Routenet with node queue. And I want to use the model in my work, however i could not find your datasets for this or trained model in your github web. Could you send me your datasets and trained model used in the paper? I really need them. Thank you in advance,sincerely.
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chuheridangwu2000
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Hello,
I am a student, and I am trying to recreate the paper, to see the results I have tried to use the notebook that you provided, there is something that I don't understand,
I see that you are normalizing the delay and traffic in the parse, and after you get the predictions you are normalizing them using:
predctions = 0.54*preds + 0.37
why is that?
and if I want to predict the jitter for example do I need to train another network with the labels being jitter?
thank you
Ayman Khiralden
Dear Sir or Madam,
We are pleased to inform you that the code for the paper "*Deep
Reinforcement Learning meets Graph Neural Networks: exploring a routing
optimization use case*" is already available in
https://github.com/knowledgedefinednetworking/DRL-GNN. Please, cite the
corresponding article if you use the code from this repository.
I hope you find it useful.
Best,
Paul
hello！I have read your paper of "Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN", but the address of the relevant network dataset is forbidden.So,can you send me the datasets can from the three links.They are about NSFNet topology,GEANT2 topology and GBN topology.Thank you!
Dear professors:
I just read the code you posted on GitHub, and I want to use the ignnition to train a RouteNet-GNN model. I see that you have already a trained_delay model, which is in .ckpt format. However, when I wanted to use your trained model, I found that the ignnition must use a ".hdf5" models. How can I transform a ".ckpt" to a ".hdf5"? Can you help me?
Looking forward to your reply! Thanks!
A student who wants to build a RouteNet GNN model.
Hello
Hope you're doing well.
I recently found the Knowledge Defined networking repository of datasets
and I'm very interested in using them to conduct link fault recovery
research in SDN. I understand that the v0 datasets have 500 rows and I have
come across the GitHub repository that explains the datasets however it has
not clearly mentioned what all attributes are captured. From the
repository, I could only find 11 attributes and the dataset has about 1961
columns. If you could please help provide me with information as to what
these columns are, I would find it extremely helpful.
Thank you so much!
--
Kind regards
Nandana
Hi,
I hope everything is going well in Spain these days. Currently, We are
working in our model to take part in the challenge and we have a question
about the format of the results we have to deliver.
As we understand you will provide a series of parameters of the simulations
you made on OMNeT++, each simulation will have N*(N-1) (every
source-destination pair) delays we have to predict, so, we know that we
have to provide a CSV with our predictions, but in what order should these
predictions be displayed in the file?
--
Regards
Jaime Vergara
Departamento Ingeniería Electrónica y de Telecomunicaciones
Universidad de Antioquia
--
"La información aquí contenida es para uso exclusivo de la persona o
entidad de destino. Está estrictamente prohibida su utilización, copia,
descarga, distribución, modificación y/o reproducción total o parcial, sin
el permiso expreso de Universidad de Antioquia, pues su contenido puede ser
de carácter confidencial y/o contener material privilegiado. Si usted
recibió esta información por error, por favor contacte en forma inmediata a
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Antioquia no es responsable por la información contenida en esta
comunicación, el directo responsable es quien la firma o el autor de la
misma."
Hi Professors:
Glad to read your papers relevant to RouteNet. It’s a great job. Is it possible for me to ask for the datasets used in the paper titiled “Towards more realistic network models based on GNNs”. I found that they were not available by now in your GitHub.
Best regards,
Qing-Qing Yang
Dear Zeyu,
Each "tar.gz" file in the datasets (e.g.,
results_nsfnetbw_10-12_Routing-SP-k-0_0_124.tar.gz) contains 125
simulations with a given routing configuration and a traffic intensity
range (<lower lambda max>-<upper lambda max>). Then, for a given traffic
intensity we generate 125 different traffic matrices and make one
simulation for each of them. The method to generate these matrices is
described in [1] (Section 4.1).
Each line in the "simulationResults.txt" file contains all the data
resulting from every simulation. In other words, all the data of a
simulation is flattened into a vector (hereafter "line"). Then, the
expression you mention points to the positions where the values
calculated by our simulator are (within the "simulationResults.txt"
file). Maybe it would be more correct to rewrite the expression as:
bandwidth[src_node][dst_node] = line[(src_node∗n+dst_node)*3]
If you want to extract the whole traffic matrix (i.e., bandwidth[:][:]),
you can make a 2-layer loop to evaluate all the combinations of
"src_node" and "dst_node" in the network (whose range is [0-13] in the
case of the nsfnet dataset).
Regarding the naming of the routing configurations, basically there are
two types of routings: "SP" are variants of the shortest path
configuration, while "W" includes longer paths than in the case of "SP".
In this context, "k_x" is just an identifier to differentiate every "SP"
or "W" configuration. However, it doesn't have any meaning about the
generation process. Lastly, "0_124" indicates that in this file there
are 125 simulations in total (i.e., [0-124]).
I hope I answered all your questions.
[1] 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 and in
ACM Symposium on SDN Research (SOSR) , pp. 140-151, 2019.
(https://arxiv.org/pdf/1901.08113.pdf)
Regards,
José
On 17/02/20 15:47, tjuzeyuluan wrote:
>
> Dear José,
> I am so appreciated that you can offer me so much guidance.
> During these days, I gradually understand the structure of dataset_v1,
> i.e. nsfnetbw.tar.gz.
>
>
> First, I imported the .ned file into omnet++ and got the
> topology as shown in the figure below.
>
>
> Second, I checked the link connection between a pair of output
> ports of its corrsponding switches as shown in the figure below.
>
>
> Third, I decoded the meaning of element in Routing.txt. Actually,
> it can be seen as a collection of routing tables of all the switches.
> For instance, line 0 represents the routing table of switch 0. For
> each row, 14 columns represent 14 destinations respectively from
> switch 0. And the element in the position (row i, column j) means, in
> switch i’s routing table, which output port of switch i should be
> chosen in order to forward a packet to arrive at destination j. For
> example in the file
> results_nsfnetbw_8-10_Routing-SP-k-0_0_124/Routing.txt, the element
> (1,2) = 1 means, in order to forward a packet to switch 2, switch 1
> should forward the packet through its output port 1.
>
> These three steps above seem to be with no doubt till now.
> However, I don’t understand the data structure yet. In the Readme file
> of GitHub
> (https://github.com/knowledgedefinednetworking/NetworkModelingDatasets/tree/…).
> It says there are 125 lines in each simulationResults.txt,
> representing 125 different levels of traffic intensity in 125 simulations.
>
> And there are 11 key performance indices listed below. For
> example, bandwidth [src_node][dst_node] seems to be a 2-layer
> for-loop, the outer layer is the index of src_node from 0-13 meanwhile
> the inner layer is the index of dst_node from 0-13. So there should be
> 14*14 = 196 elements. But what does the highlight expression mean in
> the figure snapshot?
>
>
> I thought the value should be sampled from the simulation
> environment rather than calculated. For instance, the bandwidth
> between src_node = switch 1 and dst_node = switch 4 should be recorded
> in the position bandwidth[1][4], but what does bandwidth[1][4] = (1*14
> + 4) *3 = 54 mean?
>
>
> Another question is about the naming manner of each file as
> shown below. I know 8-10 and 14-16 represent the <lower bound , upper
> bound> of traffic intensity. However, what dose SP-k-x_0 and W-1-k-x_0
> mean? Does it mean shortest path first and widest path first routing
> scheme. But how can 0 path exist?
>
>
>
> Could you please give me a bit hint to these two questions?
> Thanks a lot!
>
> Best Regards,
>
> Zeyu
>
> tjuzeyuluan
> tjuzeyuluan(a)163.com
>
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> --------- Forwarded Message ---------
>
> From： José Suárez-Varela <jsuarezv(a)ac.upc.edu>
> <mailto:jsuarezv@ac.upc.edu>
> Date： 02/17/2020 20:39
> To： tjuzeyuluan <tjuzeyuluan(a)163.com> <mailto:tjuzeyuluan@163.com>
> Cc： kdn-users(a)knowledgedefinednetworking.org
> <kdn-users(a)knowledgedefinednetworking.org>
> <mailto:kdn-users@knowledgedefinednetworking.org>
> Subject： Re: [Kdn-users] asking for Dataset
>
> Dear Zeyu,
>
> Thank you for your interest in this work. Unfortunately, the datasets
> are quite large and cannot be shared via e-mail. Note that files under
> the "nsfnetbw/tfrecords/train/" directory include 889M of data.
>
> The main difference between both datasets you mention (v0 and v1) is
> in the topologies they include (see README files with the
> descriptions). Also, 'datasets_v0' include 500 iterations for each
> combination of routing+traffic intensity. Note that each iteration
> uses a different input traffic matrix (TM) of a given traffic
> intensity (TI). The method to generate these traffic matrices is
> described in Section 4.1 of [1]. In the case of 'datasets_v1', each
> file includes 125 iterations. In this case, a file includes a
> collection of traffic matrices with a range of traffic intensities
> (<lower lambda max>-<upper lambda max>). Also, these latter datasets
> include the following information:
>
> "5.- Average per-packet neperian logarithm of the delay over the
> packets transmitted in each source-destination pair".
>
> Which can be useful to make probabilistic modeling. For instance, to
> parameterize a Gamma distribution that models the delay distribution
> on each source-destination pair.
>
> Overall, if you want to reproduce the experiments of a paper I
> recommend you use the datasets used in the paper. Otherwise, you will
> need to modify the code to read datasets with a different format. For
> instance:
>
> "Challenging the generalization capabilities of Graph Neural Networks
> for network modeling" -> datasets_v0
> (https://github.com/knowledgedefinednetworking/NetworkModelingDatasets/tree/…)
>
> Also, for the paper "Unveiling the potential of Graph Neural Networks
> for network modeling and optimization in SDN" you should use the
> datasets at the following link:
>
> https://github.com/knowledgedefinednetworking/Unveiling-the-potential-of-GN…
>
> This paper presents the first version of RouteNet
> (https://github.com/knowledgedefinednetworking/net2vec/tree/RouteNet-SOSR/ro…),
> which did not have support for variable link capacity. For this
> reason, in these latter datasets all the links in the different
> topologies have the same capacity. You can check the link capacities
> used in the "*.ned" files that describe each topology.
>
>
> [1] 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 and in ACM Symposium on SDN Research (SOSR) , pp. 140-151, 209.
> Link:
> https://github.com/knowledgedefinednetworking/Unveiling-the-potential-of-GN…
>
>
> Regards,
>
> José
>
>
> On 14/02/20 08:00, tjuzeyuluan wrote:
>>
>> Dear Professor,
>> I’m Zeyu, a PhD student from UC Berkeley. I am really interested
>> in your work related in Graph Neural Networks -based routing
>> optimization. I am trying to repeat your experiment. However, the
>> download speed from the URL(path
>> ='/home/datasets/SIGCOMM/nsfnetbw/tfrecords/train/') is so slow.
>> Could you please transfer the zip package via the e-mail? Thank you
>> very much!
>> Another question is that what’s the difference between dataset v0
>> and dataset v1. I am a little confused. Could you please explain
>> further? Thanks a lot!
>>
>> tjuzeyuluan
>> tjuzeyuluan(a)163.com
>>
>> <https://maas.mail.163.com/dashi-web-extend/html/proSignature.html?ftlId=1&n…>
>>
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>>
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