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Towards a generic deep learning pipeline for traffic measurements

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Dario RossiAuthors Info & Claims

CoNEXT-SW '21: Proceedings of the CoNEXT Student Workshop

Pages 5 - 6

https://doi.org/10.1145/3488658.3493785

Published: 07 December 2021 Publication History

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Abstract

Traffic measurements are key for network management as testified by the rich literature from both academia and industry. At their foundation, measurements rely on transformation functions f(x) = y, mapping input traffic data x to an output performance metric y. Yet, common practices adopt a bottom-up design (i.e., metric-based) which leads to (i) invest a lot of efforts into (re)discovering how to perform such mapping and (ii) create specialized solutions. For instance, sketches are a compact way to extract traffic properties (heavy-hitters, super-spreaders, etc.) but require analytical modeling to offer correctness guarantees and careful engineering to enable in-device deployment and network-wide measurements.

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Jordan Holland, Paul Schmitt, Nick Feamster, and Prateek Mittal. 2020. nprint: A standard data representation for network traffic analysis. arXiv preprint arXiv:2008.02695 (2020).

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Ozan Sener and Vladlen Koltun. 2018. Multi-Task Learning as Multi-Objective Optimization. CoRR abs/1810.04650 (2018). arXiv:1810.04650

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Mowei Wang, Yong Cui, Xin Wang, Shihan Xiao, and Junchen Jiang. 2018. Machine Learning for Networking: Workflow, Advances and Opportunities. IEEE Network 32, 2 (2018), 92--99.

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Kun Yang, Samory Kpotufe, and Nick Feamster. 2020. A Comparative Study of Network Traffic Representations for Novelty Detection. arXiv preprint arXiv:2006.16993 (2020).

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Index Terms

Towards a generic deep learning pipeline for traffic measurements
1. Computing methodologies
  1. Machine learning
2. Networks
  1. Network performance evaluation
    1. Network measurement

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Published In

CoNEXT-SW '21: Proceedings of the CoNEXT Student Workshop

December 2021

28 pages

ISBN:9781450391337

DOI:10.1145/3488658

Program Chairs:
Gareth Tyson
Queen Mary University of London
,
Hannaneh Barahouei Pasandi
Virginia Commonwealth University
,
Lars Wolf
TU Braunschweig

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Association for Computing Machinery

New York, NY, United States

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Published: 07 December 2021

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SIGCOMM

CoNEXT '21: The 17th International Conference on emerging Networking EXperiments and Technologies

December 7, 2021

Virtual Event, Germany

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Traffic shaping measurements and analyses in 3G network

Deep Learning for Network Traffic Data

Procedures and tools for analysis of network traffic measurements

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