  In this paper, we propose a Zero-Touch, deep reinforcement learning
(DRL)-based Proactive Failure Recovery framework called ZT-PFR for stateful
network function virtualization (NFV)-enabled networks. To this end, we
formulate a resource-efficient optimization problem minimizing the network cost
function including resource cost and wrong decision penalty. As a solution, we
propose state-of-the-art DRL-based methods such as soft-actor-critic (SAC) and
proximal-policy-optimization (PPO). In addition, to train and test our DRL
agents, we propose a novel impending-failure model. Moreover, to keep network
status information at an acceptable freshness level for appropriate
decision-making, we apply the concept of age of information to strike a balance
between the event and scheduling based monitoring. Several key systems and DRL
algorithm design insights for ZT-PFR are drawn from our analysis and simulation
results. For example, we use a hybrid neural network, consisting long
short-term memory layers in the DRL agents structure, to capture
impending-failures time dependency.
