Application specific slicing for MVNO through software-defined data plane enhancing SDN

In this paper, we show our prototype implementation of our proposal in the real MVNO connecting customized smartphones so that we can identify applications from the given traffic with 100% accuracy using deeply programmable nodes developed in FLARE project. In addition, we propose a new method of identifying applications from the traffic of unmodified smartphones by machine learning using the training data collected from the customized smartphones. We show that a simple machine learning technique such as random forest achieves about 80% to 96% of accuracy in application identification.