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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“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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Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498
“In this regard, there are many potential areas where DL can be applied, including online content streaming [245], routing and traffic control [246] [247], etc.”
BackgroundDeep Learning for IoT Big Data and Streaming Analytics: A Survey
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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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