Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions

Abstract

PurposeThis study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.Design/methodology/approachThe study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.FindingsFollowing the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.Originality/valueThis study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.

Review of Advancing Anomaly Detection in SDN through Deep Learning Algorithms

“…SDN employs data plane forwarding elements (switches, routers) and the controller in the control plane. [3,4] This decoupling and programmability grant network managers significant control, simplifying administration. By separating routing and forwarding activities, the control plane handles topology info and routing, while the data plane manages traffic per control unit settings.…”