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Demystifying Deep Learning in Networking

Published: 01 August 2018 Publication History

Abstract

We are witnessing a surge of efforts in networking community to develop deep neural networks (DNNs) based approaches to networking problems. Most results so far have been remarkably promising, which is arguably surprising given how intensively these problems have been studied before. Despite these promises, there has not been much systematic work to understand the inner workings of these DNNs trained in networking settings, their generalizability in different workloads, and their potential synergy with domain-specific knowledge. The problem of model opacity would eventually impede the adoption of DNN-based solutions in practice. This position paper marks the first attempt to shed light on the interpretability of DNNs used in networking problems. Inspired by recent research in ML towards interpretable ML models, we call upon this community to similarly develop techniques and leverage domain-specific insights to demystify the DNNs trained in networking settings, and ultimately unleash the potential of DNNs in an explainable and reliable way.

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      cover image ACM Other conferences
      APNet '18: Proceedings of the 2nd Asia-Pacific Workshop on Networking
      August 2018
      78 pages
      ISBN:9781450363952
      DOI:10.1145/3232565
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 01 August 2018

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      Author Tags

      1. Interpretability
      2. Neural networks
      3. Resource allocation

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      • (2024)Inferring Visibility of Internet Traffic Matrices Using eXplainable AINOMS 2024-2024 IEEE Network Operations and Management Symposium10.1109/NOMS59830.2024.10575173(1-6)Online publication date: 6-May-2024
      • (2024)Machine Learning With Computer Networks: Techniques, Datasets, and ModelsIEEE Access10.1109/ACCESS.2024.338446012(54673-54720)Online publication date: 2024
      • (2024)NetBoost: Towards efficient distillation and service differentiation of network information exposureComputer Networks10.1016/j.comnet.2024.110829255(110829)Online publication date: Dec-2024
      • (2024)Decision on Control Path: Rule-Based Policy ConversionLatency Optimization in Interactive Multimedia Streaming10.1007/978-981-97-6729-8_4(43-60)Online publication date: 30-Oct-2024
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      • (2023)Improving Performance, Reliability, and Feasibility in Multimodal Multitask Traffic Classification with XAIIEEE Transactions on Network and Service Management10.1109/TNSM.2023.324679420:2(1267-1289)Online publication date: Jun-2023
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      • (2022)Interpreting AI for Networking: Where We Are and Where We Are GoingIEEE Communications Magazine10.1109/MCOM.001.210073660:2(25-31)Online publication date: Feb-2022
      • (2022)Enabling Robust DRL-Driven Networking Systems via Teacher-Student LearningIEEE Journal on Selected Areas in Communications10.1109/JSAC.2021.312608540:1(376-392)Online publication date: Jan-2022
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