The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualize, activation and customization. Software Defined Networking (SDN)technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture, will have great potential in areas such as network resource management, route planning,traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely, the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on SDN architecture, the related ML solutions are introduced. Finally, the future development of ML algorithms and SDN concepts is discussed and analyzed. This research occupies the intersection of artificial intelligence, big data, computer networking and other disciplines; artificial intelligence itself is a new and complex interdisciplinary field,which causes the researchers in this field to often have different professional backgrounds, and sometimes,divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.
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