A survey of machine learning techniques applied to software defined networking (sdn): research issues and challenges
Abstract
In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.
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Federated learning is a technique of machine learning that trains an algorithm across several decentralized nodes or servers that do not exchange their local data samples with any centralized server.
“Federated learning [295] is a technique of machine learning that trains an algorithm across several decentralized nodes or servers that do not exchange their local data samples with any centralized server.”
From A survey on the adoption of blockchain in IoT: challenges and solutions (2021) · cites this paper as [295]
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- Authors
- Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Carleton University (CA)
- Chongqing University of Posts and Telecommunications (CN)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2019
- Volume
- 21
- Issue
- 1
- Pages
- 393-430
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2915905517
- Cited by
- 700
- References
- 274
- Citations by year
- 2026: 32 · 2025: 69 · 2024: 100 · 2023: 112 · 2022: 110 · 2021: 127 · 2020: 104 · 2019: 45 · 2018: 1
- FWCI
- 42.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Funders
- National Natural Science Foundation of China (61501042)
- Fundamental Research Funds for the Central Universities
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Keywords
- software-defined networking, network traffic classification, route optimization, quality of service prediction, quality of experience prediction, network resource management, network security, centralized network control
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