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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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“In this regard, there are many potential areas where DL can be applied, including online content streaming [285], routing and traffic control [286] [287], etc.”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [287]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
(2014) · Cited by 33,419
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Kohonen, T. (1995) · Springer series in information sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,032
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Internet of Things (IoT): A vision, architectural elements, and future directions
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
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14 of the 266 references couldn’t be matched to a known paper.
Research works printed but not linked: 20
- wang, 2016, The Applications of Deep Learning on Traffic Identification
- 2016, Methods and Apparatus for Reinforcement Learning
- 2016, Microsoft Audio Video Indexing Service (MAVIS)
- 2016, The iBrain is Here and It's Already Inside Your Phone
- 2016, The MNIST database
- 2016, Google Unveils Neural Network With ‘Superhuman’ Ability to Determine the Location of Almost Any Image
- landauer, 1987, Learning a minimally structured back propagation network to recognize speech, Proc 9th Annu Conf Cogn Sci Soc, 531
- hyndman, 2016, DataMarket Data Library (TSDL)
- 2016, Wall Street Journal-Based Continuous Speech Recognition (CSR) Corpus
- castelvecchi, 2016, Nature News Deep Learning Boosts Google Translate Tool
- 2016, The Zettabyte EraTrends and Analysis
- russon, 2016, Using Deep Learning Neural Networks to Predict Social Unrest Five Days Before it Happens
- 2016, The Zettabyte Era-Trends and Analysis
- konno, 2011, An adaptive media access control mechanism for cognitive radio, IEICE Trans Commun, j94 b, 253
- song, 2016, DeepTransport: Prediction and simulation of human mobility and transportation mode at a citywide level, Proc IJCAI, 2618
- rondeau, 2004, Cognitive radios with genetic algorithms: Intelligent control of software defined radios, Software Defined Radio Forum Technical Conference, 3c
- chintala, 2016, Understanding Natural Language with Deep Neural Networks Using Torch
- 2016, WILL API
- 2016, Deep Learning Comp Sheet Deeplearning4j vs Torch vs Theano vs Caffe vs TensorFlow
- 2016, Intel Autonomous Cars
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Sharma, N., & Panwar, D. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-15-6707-0_17 · Cited by 4
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Valcarenghi, L., Pacini, A., Sgambelluri, A., & Paolucci, F. (2021) · International Conference on Optical Network Design and Modeling (ONDM) · doi:10.23919/ondm51796.2021.9492488 · Cited by 4
Ruan, L., & Wong, E. (2023) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn58024.2023.10230123 · Cited by 4
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Contreras, L. M., López, V., Vilalta, R., Casellas, R., Muñoz, R., Jiang, W., Schotten, H. D., Calero, J. M. A., Wang, Q., Sonkoly, B., et al. (2018) · doi:10.1002/9781119425144.ch10 · Cited by 4
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Zhong, S., & Liu, X. (2025) · IEEE Access · doi:10.1109/access.2025.3588506 · Cited by 3
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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)
- ORCID
- 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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