The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective
Abstract
Recently, deep learning, an emerging machine learning technique, is garnering a lot of research attention in several computer science areas. However, to the best of our knowledge, its application to improve heterogeneous network traffic control (which is an important and challenging area by its own merit) has yet to appear because of the difficult challenge in characterizing the appropriate input and output patterns for a deep learning system to correctly reflect the highly dynamic nature of large-scale heterogeneous networks. In this vein, in this article, we propose appropriate input and output characterizations of heterogeneous network traffic and propose a supervised deep neural network system. We describe how our proposed system works and how it differs from traditional neural networks. Also, preliminary results are reported that demonstrate the encouraging performance of our proposed deep learning system compared to a benchmark routing strategy (Open Shortest Path First (OSPF)) in terms of significantly better signaling overhead, throughput, and delay.
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There are many potential areas where DL can be applied, including online content streaming, routing and traffic control, etc.
“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 [286]
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“applied, including online content streaming [285], routing and traffic control [286], [287], etc.”
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Self-citation
- Authors
- Kato, N., Fadlullah, Z. M., Mao, B., Tang, F., Akashi, O., Inoue, T., & Mizutani, K.
- Affiliations
- Tohoku University (JP)
- NTT (Japan) (JP)
- ORCID
- Venue
- IEEE Wireless Communications
- Published
- 2017-06
- Volume
- 24
- Issue
- 3
- Pages
- 146-153
- ISSN
- 1536-1284 (ISSN-L)
- 1558-0687
- OpenAlex
- W2566425973
- Cited by
- 400
- References
- 0
- Citations by year
- 2026: 5 · 2025: 21 · 2024: 22 · 2023: 33 · 2022: 34 · 2021: 51 · 2020: 88 · 2019: 82 · 2018: 56 · 2017: 8
- FWCI
- 45.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Wireless Communications (publishedVersion)
- 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
- heterogeneous networks, network traffic management, deep learning, deep neural network, routing, OSPF, signaling overhead, throughput, delay
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