  Software defined networking (SDN) and network functions virtualisation (NFV)
are making networks programmable and consequently much more flexible and agile.
To meet service level agreements, achieve greater utilisation of legacy
networks, faster service deployment, and reduce expenditure, telecommunications
operators are deploying increasingly complex service function chains (SFCs).
Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism
from the cloud to the edge introduces significant SFC placement challenges, not
least adding or removing network functions while maintaining availability,
quality of service, and minimising cost. In this paper, an availability- and
energy-aware solution based on reinforcement learning (RL) is proposed for
dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic
(A2C) and Proximal Policy Optimisation (PPO2), are compared using simulations
of a ground truth network topology based on the Rede Nacional de Ensino e
Pesquisa (RNP) Network, Brazil's National Teaching and Research Network
backbone. The simulation results showed that PPO2 generally outperformed A2C
and a greedy approach both in terms of acceptance rate and energy consumption.
A2C outperformed PPO2 only in the scenario where network servers had a greater
number of computing resources.
