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

TLDR

The rapid growth of heterogeneous networking infrastructure and the distributed nature of traditional networks make deploying machine learning difficult, but software‑defined networking’s centralized control, global view, and dynamic rule updates provide a conducive environment for intelligent network management. This paper surveys the literature on machine‑learning algorithms applied to software‑defined networking. The survey first reviews related works and background, then presents an overview of machine‑learning algorithms, and finally examines their application to SDN in traffic classification, routing optimization, QoS/QoE prediction, resource management, and security, concluding with challenges and future directions.

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.