Knowledge-Defined Networking Training Datasets

This website aims to publish training datasets to encourage open research, development and benchmarking of Machine Learning algorithms applied to Computer Networks.

Network modeling datasets

Datasets v0

Dataset description

NSFNet Topology

Name	Nodes	Size	MD5Sum
nsfnet.tar.gz	14	2.0 GB	d1c2bf8d461b571f413f9df4ec845c33

GEANT2 Topology

Name	Nodes	Size	MD5Sum
geant2.tar.gz	24	6 GB	4847eb2c4c14c6c2e1ba82287ffc7365

50 Nodes Topology

Name	Nodes	Size	MD5Sum
synth50.tar.gz	50	29.0 GB	993e8cbe8f897235add3ae9aa1c47a82

Datasets v1

Dataset description

NSFNet Topology

Name	Nodes	Size	MD5Sum
nsfnetbw.tar.gz	14	1.1 GB	5d800b709721925fcc291b9e4eb2da11

GBN Topology

Name	Nodes	Size	MD5Sum
gbnbw.tar.gz	17	1.7 GB	efe9ce8aabda216e93b3bc41c44cbc22

GEANT2 Topology

Name	Nodes	Size	MD5Sum
geant2bw.tar.gz	24	3.3 GB	51a995fd6e461cbb8008407da44f8737

Germany 50 Nodes Topology

Name	Nodes	Size	MD5Sum
germany50bw.tar.gz	50	15.8 GB	b738280819bccfadcca1be57085bc69e

Datasets papers

Virtual Network Functions (VNFs):

Name	Size	Description	Includes
OVS.zip	527 kB	CPU consumption of an OVS connected to a SDN controller. See further details in the KDN paper	Dataset (traffic features + cpu consuption), scripts to read the dataset, readme.txt
Firewall.zip	530 kB	CPU consumption of an OVS configured with firewall rules. See further details in the KDN paper	Dataset (traffic features + cpu consuption), scripts to read the dataset, readme.txt
Snort.zip	532 kB	CPU consumption of a SNORT with the initial configuration. See further details in the KDN paper	Dataset (traffic features + cpu consuption), scripts to read the dataset, readme.txt

Network Characterization:

Name	Size	Description	Includes
overUnderNetwork.zip	17.35 MB	Flow-level delay in a overlay-underlay network with load balancing in the overlay	Dataset (traffic + load balancing + delay), readme.txt
star.zip	670.8 MB	Delay among pairs of nodes in a star network	Dataset (traffic + delay), readme.txt
ring.zip	671.6 MB	Delay among pairs of nodes in a ring network	Dataset (traffic + delay), readme.txt
scaleFree.zip	911.8 MB	Delay among pairs of nodes in a scale free network	Dataset (traffic + delay), readme.txt
overUnder.zip	907.1 MB	Delay among pairs of nodes in a overlay-underlay network	Dataset (traffic + delay), readme.txt

Unveiling the potential of GNN for network modeling and optimization in SDN: Paper

Name	Size	Description
nsfnet.zip	1.3 GB	Datasets of the NFSNet topology (This dataset was randomly partitioned to obtain the training and evaluation datasets)
geant2.zip	5.2 GB	Datasets of GEANT2 topology (Used only for evaluation)
GBN.zip	2.8 GB	Dataset of the GBN topology (Used only for evaluation)
trained_model_delay.zip	3.0 MB	RouteNet model for delay trained with the NSFNet topology dataset
trained_model_jitter.zip	1.0 MB	RouteNet model for jitter trained with the NSFNet topology dataset (This model was trained from a model of the delay in an early training stage. See further details in the paper)
README_gnn.pdf	35 KB	Description of the datasets

Understanding the Network Modeling of Computer Networks:

Name	Size	Description	Includes
routing.zip	120.3 MB	Flow-level delay in a scale-free network with 4 different routings	Dataset (traffic + delay)
saturation.zip	1.4 GB	Delay among pairs of nodes a 10-nodes scale-free network changing the traffic intensity and traffic distribution	Dataset (traffic + delay), readme.txt
netSize.zip	46.0 MB	Delay among pairs of nodes in a 5, 10 and 15 nodes scale-free network.	Dataset (traffic + delay)
topologies.zip	1.3 GB	Delay among pairs of nodes different topologies and sizes.	Dataset (traffic + delay), readme.txt

Neural Nets:

Name	Size	Description	Includes
codeMatlab.zip	916 kB	Matlab code used to train the VNF data and the figures from the experiment described in the KDN paper	Code, readme.txt
codePython.zip	6 kB	Python code used to train the overUnderNetwork data	Code, readme.txt

Ready to use examples:

Name	Size	Description	Includes
KDN_overlayUnderlay.zip	18,2 MB	Code, dataset and setup fot the overlay-underlay experiment described in KDN paper	Ready to set up and run

A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

Name	Size	Description	Includes
DRL.zip	43 KB	Source code for the Deep-Reinforcement Learning agent and OMNeT++ network topology	Code, readme.md
train.csv.gz	34 KB	Evolution of DRL agent's performance during training, measured in average network delay	Dataset
benchmark.csv.gz	482 MB	Performance benchmark using 1000000 routings times 1000 traffic matrices, measured in average network delay	Dataset
traffic.zip	587 KB	1000 test traffic matrixes (100 per each traffic intensity)	Dataset
100k.csv.gz	4,3 MB	100000 test routing configurations	Dataset

Papers:

José Suárez-Varela, Albert Mestres, Junlin Yu, Li Kuang, Haoyu Feng, Albert Cabellos-Aparicio, Pere Barlet-Ros; "Routing in Optical Transport Networks with Deep Reinforcement Learning," in Journal of Optical Communications and Networking, vol. 11, pp 547-558, Sept 2019
José Suárez-Varela, Sergi Carol-Bosch, Krzysztof Rusek, Paul Almasan, Marta Arias, Pere Barlet-Ros, Albert Cabellos-Aparicio; "Challenging the generalization capabilities of Graph Neural Networks for network modeling," in ACM SIGCOMM Posters and Demos, August 2019
José Suárez-Varela, Albert Mestres, Junlin Yu, Li Kuang, Haoyu Feng, Pere Barlet-Ros, Albert Cabellos-Aparicio; "Feature Engineering for Deep Reinforcement Learning Based Routing," in IEEE International Conference on Communications (ICC), May 2019
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 https://arxiv.org/abs/1901.08113 and in Proceedings of ACM Symposium on SDN Research (SOSR), pp. 140-151, April 2019.
José Suárez-Varela, Albert Mestres, Junlin Yu, Li Kuang, Haoyu Feng, Pere Barlet-Ros, Albert Cabellos-Aparicio; "Routing based on Deep Reinforcement Learning in Optical Transport Networks," in Proceedings of the Optical Fiber Communication Conference (OFC), San Diego, USA, March 2019
Giorgio Stampa, Marta Arias, David Sanchez-Charles, Victor Muntes-Mulero, Albert Cabellos; "A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization" in https://arxiv.org/abs/1709.07080
Albert Mestres, Eduard Alarcón, Yusheng Ji, Albert Cabellos-Aparicio; "Understanding the Modeling of Computer Network Delays using Neural Networks," in Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks ACM, August 2018
Albert Mestres, Eduard Alarcón, Albert Cabellos, "A machine learning-based approach for virtual network function modeling", in Wireless Communications and Networking Conference Workshops (WCNCW), 2018 IEEE, April
Albert Mestres, Alberto Rodriguez-Natal, Josep Carner, Pere Barlet-Ros, Eduard Alarcón, Marc Solé, Victor Muntés, David Meyer, Sharon Barkai, Mike J Hibbett, Giovani Estrada, Florin Coras, Vina Ermagan, Hugo Latapie, Chris Cassar, John Evans, Fabio Maino, Jean Walrand, Albert Cabellos; "Knowledge-Defined Networking," in http://arxiv.org/abs/1606.06222 and in ACM SIGCOMM Computer Communication Review, vol. 47, number 3, pp. 2-10, July 2017
Josep Carner, Albert Mestres, Eduard Alarcón, Albert Cabellos, "Machine learning-based network modeling: An artificial neural network model vs a theoretical inspired model", in Ubiquitous and Future Networks (ICUFN), 2017 Ninth International Conference on, July 2017

Contact:

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Albert Cabellos: