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

TitreMean-Field Game and Reinforcement Learning MEC Resource Provisioning for SFC
Publication TypeConference Paper
Year of Publication2021
AuthorsAbouaomar, A, Cherkaoui, S, Mlika, Z, Kobbane, A
Conference Name2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
Mots-clésComputer games, Computing infrastructures, Computing resource, e-learning, Edge computing, Markov processes, Mean-field games, Multi-access edge computing, Multiaccess, Network function virtualization, Reinforcement learning, Reinforcement learnings, Resource provisioning, Service function chaining, Service functions, Transfer functions, Virtual addresses, Virtual reality

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. © 2021 IEEE.




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