Predictive Flow Modeling in Software Defined Network
Abha Kumari, Joydeep Chandra, Ashok Singh Sairam · 2019
The centralized control plane of Software Defined Network (SDN) introduces scalability concerns, which is addressed by physically distributing the control plane, although logically centralized. As the task of the control plane is delegated to multiple controllers, the layout of the controllers greatly influence the performance of the network. An important objective that researchers try to optimize while deciding placement of controllers is the flow setup time. This, in turn, depends on the number of flows generated. In this work, we decompose the network traffic into a time series model of flows. We use parametric and non-parametric learning techniques to predict the number of flows for the next epoch based on the current traffic dynamics. Two different real traffic traces have been used to develop the prediction models.