In this paper, we apply the concept of software-defined data plane to defining new services for Mobile Virtual Network Operators (MVNOs). Although there are a large number of MVNOs proliferating all over the world and most of them provide low bandwidth at low price, we propose a new business model for MVNOs and empower them with capability of tailoring fine-grained subscription plans that can meet users' demands. For example, abundant bandwidth can be allocated for some specific applications, while the rest of the applications are limited to low bandwidth. For this purpose, we have recently proposed the concept of application and/or device specific slicing that classifies application and/or device specific traffic into slices and applies fine-grained quality of services (QoS), introducing various applications of our proposed system [9]. This paper reports the prototype implementation of such proposal in the real MVNO connecting customized smartphones so that we can identify applications from the given traffic with 100% accuracy. 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 achives about 80% of accuracy in applicaton identification.