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

TL;DR

This paper introduces a novel mean-field game framework combined with reinforcement learning to optimize resource provisioning for service function chaining in MEC, reducing delay and outperforming existing methods.

Contribution

It proposes a new MFG-based model for VNF placement and chaining, and applies reinforcement learning to learn optimal policies without system control parameters.

Findings

Outperforms benchmark approaches in simulations

Reduces service delay in MEC environments

Provides a scalable solution for VNF resource management

Abstract

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.