论文标题
平均场游戏和强化学习MEC资源为SFC提供
Mean-Field Game and Reinforcement Learning MEC Resource Provisioning for SFC
论文作者
论文摘要
在本文中，我们根据虚拟网络功能（VNF）在多访问边缘计算（MEC）基础架构中解决服务功能链接（SFC）的资源供应问题（SFC），以减少服务延迟。我们将VNF视为系统的主要实体，并提出一个平均场景游戏（MFG）框架，以模拟其位置和链接的行为。然后，为了实现最佳资源供应策略而无需考虑系统控制参数，我们将提出的MFG减少到马尔可夫决策过程（MDP）。通过这种方式，我们利用参与者批评的方法来利用强化学习来学习复杂的放置和链接政策。仿真结果表明，我们提出的方法的表现优于基准的最先进方法。
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.
