A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

By Jun-feng Xie, F. Yu, Tao Huang, Renchao Xie, Jiang Liu, Chen-meng Wang, Yun-jie Liu. IEEE Communications Surveys and Tutorials, 2019

524 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/a42e380e1b8aafecb3b1e338a8a9a579c6a5a40f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “such as sensor networks [53], cognitive radio networks [52], machine-to-machine (M2M) communication [3], physical layer design [32], software defined networking [55], Internet”
Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey (2020, methodology): “To elaborate, in contrast to distributed networks, where every network node makes its own decisions based on the locally configured routing tables, in SDNs, centralized control nodes continuously monitor the network’s traffic flow and manage the network’s configuration in order to improve its overal”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “Moreover, ML techniques applied to software defined networking (SDN) were investigated in [36], from the perspective of traffic classification, routing optimization, resource management, etc.”