Raphaël Azorin
Massimo Gallo
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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Digital Library
- 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).Google Scholar
- Ozan Sener and Vladlen Koltun. 2018. Multi-Task Learning as Multi-Objective Optimization. CoRR abs/1810.04650 (2018). arXiv:1810.04650 Google ScholarDigital Library
- 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. Google ScholarCross Ref
- Kun Yang, Samory Kpotufe, and Nick Feamster. 2020. A Comparative Study of Network Traffic Representations for Novelty Detection. arXiv preprint arXiv:2006.16993 (2020).Google Scholar
Index Terms
- Towards a generic deep learning pipeline for traffic measurements
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