Traffic-aware dynamic controller placement in SDN using NFV

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are promising technologies for delivering software-based networks to the user community. The application of Machine Learning (ML) in SDN and NFV enables innovation and easiness towards network management. The shift towards the softwarization of networks broadens the many doors of innovation and challenges. As the number of devices connected to the Internet is increasing swiftly, the SDNFV traffic management mechanism will provide a better solution. Many ML techniques applied to SDN focus more on the classification problems like network attack patterns, routing techniques, QoE/QoS provisioning. The approach of the application of ML to SDNFV and SDN controller placement has a lot of scope to explore. This work aims to develop an ML approach for network traffic management by predicting the number of controllers likely to be placed in the network. The proposed prediction mechanism is a centralized one and deployed as Virtual Network Function (VNF) in the NFV environment. The number of controllers is estimated using the predicted traffic and placed in the optimal location using the K-Medoid algorithm. The proposed method is suitably analysed for performances metrics. The proposed approach effectively combines SDN, NFV and ML for the better achievement of network automation.

Automated summary

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are promising technologies for delivering software-based networks. The application of Machine Learning (ML) in SDN and NFV enables innovative and easy network management. This research aims to develop an ML approach for network traffic management by predicting the number of controllers and placing them optimally.