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

Mean-Field Game and Reinforcement Learning MEC Resource Provisioning for SFC