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Cloudification of all computing environments is an undergoing process. The process has overpassed the classical Virtual Machines (VM) and Software-Defined Networking (SDN) approach and has moved towards dockizing, microservices, app functions, network functions etc. 5G penetration is another trend, and it is built on such platforms. In this environment we are investigating the efficiency of supervised machine learning algorithms for classification of regular and encrypted Voice over IP (VoIP) traffic that 5G relies on, within a virtualized Network Functions Virtualization (NFV) environment and an east-west based network traffic. We are using statistical methods for classification of network packets without the need of inspecting the payload data and without the source, destination and port information of the packets. The efficiency is analyzed from a point of precision of the classification, but also from a point of time consumption, as adding delay to the original traffic may cause a problem, especially within 5G environments where packet delay is crucial.

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Distribution of network functions virtualization (NFV) environment on multiple locations and datacenters is a hot topic in research and engineering communities. In this article, we are making analytical models to calculate the network packet sojourn time in three different scenarios: classical NFV environment where all NFV elements are on the same location, distributed architecture of the data plane with centralized management and orchestration (MANO) environment, and distributed environment where every location has its own data plane and MANO elements. The analytical models are tested and discussed through Matlab based simulations. Through the simulation results, we discuss the impact of changes of different parameters on the packet sojourn time, and we compare the architectures in order to see how they can be fitted in practical services provided in an NFV environment.