This website aims to publish training datasets to encourage open research, development and benchmarking of Machine Learning algorithms applied to Computer Networks.
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| Name | Nodes | Size | MD5Sum |
|---|---|---|---|
| nsfnet.tar.gz | 14 | 2.0 GB | d1c2bf8d461b571f413f9df4ec845c33 |
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| Name | Nodes | Size | MD5Sum |
|---|---|---|---|
| geant2.tar.gz | 24 | 6 GB | 4847eb2c4c14c6c2e1ba82287ffc7365 |
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| Name | Nodes | Size | MD5Sum |
|---|---|---|---|
| synth50.tar.gz | 50 | 29.0 GB | 993e8cbe8f897235add3ae9aa1c47a82 |
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| Name | Nodes | Size | MD5Sum |
|---|---|---|---|
| nsfnetbw.tar.gz | 14 | 1.1 GB | 5d800b709721925fcc291b9e4eb2da11 |
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| Name | Nodes | Size | MD5Sum |
|---|---|---|---|
| gbnbw.tar.gz | 17 | 1.7 GB | efe9ce8aabda216e93b3bc41c44cbc22 |
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| Name | Nodes | Size | MD5Sum |
|---|---|---|---|
| geant2bw.tar.gz | 24 | 3.3 GB | 51a995fd6e461cbb8008407da44f8737 |
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| Name | Nodes | Size | MD5Sum |
|---|---|---|---|
| germany50bw.tar.gz | 50 | 15.8 GB | b738280819bccfadcca1be57085bc69e |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
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
Site maintained by Unviersitat Politècnica de Catalunya, feel free to contribute with your training sets by sending an email to:
| Albert Cabellos: |  |