A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks
| Autor: | Amine Abouaomar, Abdellatif Kobbane, Zoubeir Mlika, Abderrahime Filali, Soumaya Cherkaoui |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Networking and Internet Architecture (cs.NI)
FOS: Computer and information sciences Vehicular ad hoc network Computer science business.industry Quality of service Q-learning Markov process Computer Science - Networking and Internet Architecture symbols.namesake Server symbols Reinforcement learning Markov decision process business Edge computing Computer network |
| Zdroj: | LCN |
| DOI: | 10.1109/lcn52139.2021.9524882 |
| Popis: | Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MEC-enabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|