  In this paper, we address the resource provisioning problem for service
function chaining (SFC) in terms of the placement and chaining of virtual
network functions (VNFs) within a multi-access edge computing (MEC)
infrastructure to reduce service delay. We consider the VNFs as the main
entities of the system and propose a mean-field game (MFG) framework to model
their behavior for their placement and chaining. Then, to achieve the optimal
resource provisioning policy without considering the system control parameters,
we reduce the proposed MFG to a Markov decision process (MDP). In this way, we
leverage reinforcement learning with an actor-critic approach for MEC nodes to
learn complex placement and chaining policies. Simulation results show that our
proposed approach outperforms benchmark state-of-the-art approaches.
