Cited by 810Software-Defined Networks and 5GNetwork Traffic and Congestion ControlInternet Traffic Analysis and Secure E-voting

The DOI matches its Crossref record; 252 of the 266 references it cites were found in a registry or an index.

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Abstract

Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.

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Crossref · 10.1109/comst.2017.2707140

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Research works printed but not linked: 20

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  7. landauer, 1987, Learning a minimally structured back propagation network to recognize speech, Proc 9th Annu Conf Cogn Sci Soc, 531
  8. hyndman, 2016, DataMarket Data Library (TSDL)
  9. 2016, Wall Street Journal-Based Continuous Speech Recognition (CSR) Corpus
  10. castelvecchi, 2016, Nature News Deep Learning Boosts Google Translate Tool
  11. 2016, The Zettabyte EraTrends and Analysis
  12. russon, 2016, Using Deep Learning Neural Networks to Predict Social Unrest Five Days Before it Happens
  13. 2016, The Zettabyte Era-Trends and Analysis
  14. konno, 2011, An adaptive media access control mechanism for cognitive radio, IEICE Trans Commun, j94 b, 253
  15. song, 2016, DeepTransport: Prediction and simulation of human mobility and transportation mode at a citywide level, Proc IJCAI, 2618
  16. rondeau, 2004, Cognitive radios with genetic algorithms: Intelligent control of software defined radios, Software Defined Radio Forum Technical Conference, 3c
  17. chintala, 2016, Understanding Natural Language with Deep Neural Networks Using Torch
  18. 2016, WILL API
  19. 2016, Deep Learning Comp Sheet Deeplearning4j vs Torch vs Theano vs Caffe vs TensorFlow
  20. 2016, Intel Autonomous Cars

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Metadata

Authors
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K.
Affiliations
  • Tohoku University (JP)
  • NTT (Japan) (JP)
Venue
IEEE Communications Surveys & Tutorials
Published
2017
Volume
19
Issue
4
Pages
2432-2455
ISSN
1553-877X (ISSN-L)
OpenAlex
W2617931713
Cited by
810
References
266
Citations by year
2026: 25 · 2025: 46 · 2024: 71 · 2023: 69 · 2022: 82 · 2021: 122 · 2020: 170 · 2019: 135 · 2018: 80 · 2017: 10
FWCI
71.94
Type
journal article
Language
en
Open access
closed
Locations
IEEE Communications Surveys & Tutorials (publishedVersion)
Funders
Japan Society for the Promotion of Science (16H05858)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
  • Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
Keywords
deep learning, network traffic management, intelligent routing, autonomous network management, deep learning architectures, network traffic growth, packet-switched networks

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