research-article

In-Network Machine Learning Using Programmable Network Devices: A Survey

Authors:

Changgang Zheng,

Shay Vargaftik,

Yaniv Ben-Itzhak,

Noa ZilbermanAuthors Info & Claims

IEEE Communications Surveys & Tutorials, Volume 26, Issue 2

Pages 1171 - 1200

https://doi.org/10.1109/COMST.2023.3344351

Published: 19 December 2023 Publication History

Abstract

Machine learning is widely used to solve networking challenges, ranging from traffic classification and anomaly detection to network configuration. However, machine learning also requires significant processing and often increases the load on both networks and servers. The introduction of in-network computing, enabled by programmable network devices, has allowed to run applications within the network, providing higher throughput and lower latency. Soon after, in-network machine learning solutions started to emerge, enabling machine learning functionality within the network itself. This survey introduces the concept of in-network machine learning and provides a comprehensive taxonomy. The survey provides an introduction to the technology and explains the different types of machine learning solutions built upon programmable network devices. It explores the different types of machine learning models implemented within the network, and discusses related challenges and solutions. In-network machine learning can significantly benefit cloud computing and next-generation networks, and this survey concludes with a discussion of future trends.

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Cited By

Zang MZheng CKoziak TZilberman NDittmann L(2024)Federated In-Network Machine Learning for Privacy-Preserving IoT Traffic AnalysisACM Transactions on Internet Technology10.1145/369635424:4(1-24)Online publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1145/3696354
Datta SKotha AVenkanna UMallikharjuna Rao K(2024)XNetIoT: An Extreme Quantized Neural Network Architecture for IoT Environment Using P4IEEE Transactions on Network and Service Management10.1109/TNSM.2024.342331721:5(5756-5767)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3423317
Zhang KSamaan NKarmouch A(2024)A Machine Learning-Based Toolbox for P4 Programmable Data-PlanesIEEE Transactions on Network and Service Management10.1109/TNSM.2024.340207421:4(4450-4465)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3402074

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cover image IEEE Communications Surveys & Tutorials

IEEE Communications Surveys & Tutorials Volume 26, Issue 2

Secondquarter 2024

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Zang MZheng CKoziak TZilberman NDittmann L(2024)Federated In-Network Machine Learning for Privacy-Preserving IoT Traffic AnalysisACM Transactions on Internet Technology10.1145/369635424:4(1-24)Online publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1145/3696354
Datta SKotha AVenkanna UMallikharjuna Rao K(2024)XNetIoT: An Extreme Quantized Neural Network Architecture for IoT Environment Using P4IEEE Transactions on Network and Service Management10.1109/TNSM.2024.342331721:5(5756-5767)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3423317
Zhang KSamaan NKarmouch A(2024)A Machine Learning-Based Toolbox for P4 Programmable Data-PlanesIEEE Transactions on Network and Service Management10.1109/TNSM.2024.340207421:4(4450-4465)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3402074

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