Active Few-shot Learning For RouteNet-Fermi
Pages 19 - 24
Abstract
Machine-learning-based Network Modeling requires a compact training data set that contains diversified network topology and configurations covering different congestion levels. We formalize the problem of modeling network delay using Multi-stage Message Passing Graph Neural Networks (GNNs) under the constraints of a limited number of training samples and a limited number of nodes for the topology of each sample as a few-shot learning problem. To tackle it, we propose an active learning algorithm that selectively randomizes initial features that are invariant of node numbers and then uses pool-based uncertainty sampling for selecting the approximated optimal network topology based on the Shannon entropy. The approximation could be theoretically proven and confirmed with experimental results.
References
[1]
Paul Almasan et al. 2022. Network digital twin: context, enabling technologies, and opportunities. IEEE Communications Magazine, 60, 11, 22--27.
[2]
Sagie Benaim and Lior Wolf. 2018. One-shot unsupervised cross domain translation. advances in neural information processing systems, 31.
[3]
Sergi Caelles, Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, and Luc Van Gool. 2017. One-shot video object segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 221--230.
[4]
Rich Caruana. 1997. Multitask learning. Machine learning, 28, 41--75.
[5]
Miquel Ferriol-Galmés, Jordi Paillisse, José Suárez-Varela, Krzysztof Rusek, Shihan Xiao, Xiang Shi, Xiangle Cheng, Pere Barlet-Ros, and Albert Cabellos- Aparicio. 2023. Routenet-fermi: network modeling with graph neural networks. IEEE/ACM Transactions on Networking.
[6]
Miquel Ferriol-Galmés, Krzysztof Rusek, José Suárez-Varela, Shihan Xiao, Xiang Shi, Xiangle Cheng, BoWu, Pere Barlet-Ros, and Albert Cabellos-Aparicio. 2022. Routenet-erlang: a graph neural network for network performance evaluation. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, 2018--2027.
[7]
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. 2012. A kernel two-sample test. The Journal of Machine Learning Research, 13, 1, 723--773.
[8]
Albert Mestres et al. 2017. Knowledge-defined networking. SIGCOMM Comput. Commun. Rev., 47, 3, (Sept. 2017), 2--10.
[9]
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2018. Foundations of machine learning. MIT press.
[10]
Thomas Müller, Guillermo Pérez-Torró, Angelo Basile, and Marc Franco-Salvador. 2022. Active few-shot learning with fasl. In International Conference on Applications of Natural Language to Information Systems. Springer, 98--110.
[11]
David Pujol-Perich, José Suárez-Varela, Miquel Ferriol, Shihan Xiao, Bo Wu, Albert Cabellos-Aparicio, and Pere Barlet-Ros. 2021. Ignnition: bridging the gap between graph neural networks and networking systems. IEEE network, 35, 6, 171--177.
[12]
Krzysztof Rusek, José Suárez-Varela, Paul Almasan, Pere Barlet-Ros, and Albert Cabellos-Aparicio. 2020. Routenet: leveraging graph neural networks for network modeling and optimization in sdn. IEEE Journal on Selected Areas in Communications, 38, 10, 2260--2270.
[13]
Burr Settles. 2009. Active learning literature survey.
[14]
José Suárez-Varela et al. 2021. The graph neural networking challenge: a worldwide competition for education in ai/ml for networks. ACM SIGCOMM Computer Communication Review, 51, 3, 9--16.
[15]
Andras Varga. 2010. Omnet++. In Modeling and tools for network simulation. Springer, 35--59.
[16]
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni. 2020. Generalizing from a few examples: a survey on few-shot learning. ACM computing surveys (csur), 53, 3, 1--34.
[17]
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec. 2018. Graphrnn: generating realistic graphs with deep auto-regressive models. In International conference on machine learning. PMLR, 5708--5717.
Index Terms
- Active Few-shot Learning For RouteNet-Fermi
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Information
Published In
December 2023
49 pages
ISBN:9798400704482
DOI:10.1145/3630049
- General Chairs:
- Pere Barlet-Ros,
- Pedro Casas,
- Franco Scarselli,
- Jose Suarez-Varela,
- Albert Cabellos
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Publication History
Published: 05 December 2023
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Author Tags
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- Research-article
Funding Sources
- Guangzhou Municipal Science and Technology Project
- Guangdong Provincial Department of Education Major Research Project
- National Nature Science Foundation of China (NSFC) Grant
Conference
CoNEXT 2023
Sponsor:
CoNEXT 2023: The 19th International Conference on emerging Networking EXperiments and Technologies
December 8, 2023
Paris, France
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