Abstract
The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, 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 the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI 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.
Highlights
Today, networks are becoming more heterogeneous and complex
From the perspective of machine learning (ML) algorithms, we directly introduce different types of ML algorithms combined with the Software-defined networking (SDN)-concept networks applications in Section II, which will be useful for researchers with professional ML backgrounds to understand applications in SDN
Our work focuses on the multidisciplinary characteristics of the ML algorithms and SDN network applications, and introduces the current research from two perspectives
Summary
Networks are becoming more heterogeneous and complex. It is urgent for networks to optimize traffic distribution and manage a large number of devices. Network traffic control and management is implemented based on ML methods in the SDN architecture. In this paper, according to the different application scenarios, we divide the existing application cases into five categories, namely, resource management and allocation, flow and traffic processing, system security guarantees, theoretic architecture approaches and parameter modeling and promotion in multimedia content services To provide more thorough understanding of this research, Sultana et al [83] recently reviewed various recent studies on SDN-based ML methods to implement Network Intrusion Detection Systems (NIDS) These authors evaluated the features of various ML algorithms, including supervised learning, unsupervised learning, and semi-supervised learning. We believe that there will be more research on high-level applications with the continuous development of infrastructure, resource management, and other basic applications
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