Name
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 actorcritic 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/GLOBECOM46510.2021.9685236 | 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) |
Keywords | DocType | ISSN |
Multi-Access Edge computing, Virtual Network Functions, Resource provisioning, Service Function Chaining, Reinforcement Learning | Conference | 2334-0983 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amine Abouaomar | 1 | 3 | 1.71 |
| Soumaya Cherkaoui | 2 | 0 | 0.34 |
| Zoubeir Mlika | 3 | 13 | 3.84 |
| Abdellatif Kobbane | 4 | 126 | 27.54 |