  It is expected that the next generation cellular networks provide a connected
society with fully mobility to empower the socio-economic transformation.
Several other technologies will benefits of this evolution, such as Internet of
Things, smart cities, smart agriculture, vehicular networks, healthcare
applications, and so on. Each of these scenarios presents specific requirements
and demands different network configurations. To deal with this heterogeneity,
virtualization technology is key technology. Indeed, the network function
virtualization (NFV) paradigm provides flexibility for the network manager,
allocating resources according to the demand, and reduces acquisition and
operational costs. In addition, it is possible to specify an ordered set of
network virtual functions (VNFs) for a given service, which is called as
service function chain (SFC). However, besides the advantages from service
virtualization, it is expected that network performance and availability do not
be affected by its usage. In this paper, we propose the use of reinforcement
learning to deploy a SFC of cellular network service and manage the VNFs
operation. We consider that the SFC is deployed by the reinforcement learning
agent considering a scenarios with distributed data centers, where the VNFs are
deployed in virtual machines in commodity servers. The NFV management is
related to create, delete, and restart the VNFs. The main purpose is to reduce
the number of lost packets taking into account the energy consumption of the
servers. We use the Proximal Policy Optimization (PPO) algorithm to implement
the agent and preliminary results show that the agent is able to allocate the
SFC and manage the VNFs, reducing the number of lost packets.
