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MEC-Based Dynamic Controller Placement in SD-IoV: A Deep Reinforcement Learning Approach.
- Source :
- IEEE Transactions on Vehicular Technology; Sep2022, Vol. 71 Issue 9, p10044-10058, 15p
- Publication Year :
- 2022
-
Abstract
- The flow fluctuations in the highly dynamic Internet of Vehicles (IoV) make the IoV difficult to provide reliable and scalable wireless network services for the emerging applications in the 5 G and beyond era. The software-defined networks (SDN) could feasibly manage and optimize the network according to the network load. Controller placement is a critical problem in SDN to achieve its robustness and flexibility with the changes of network status. Motivated by the advantages of SDN and Mobile-edge computing (MEC), this paper aims at enhancing the performance of IoV with the assistance of these two. Specifically, we consider a three-layer hierarchical SDN control plane for the IoV, where the SDN controllers are placed at the edge of networks. Under this framework, we investigate a multi-objective optimization problem on controller placement problem including delay, load balancing, and path reliability. To efficiently solve the formulated NP-hard problems, we develop an algorithm based on multi-agent deep Q-learning networks (MADQN) because of its advantages for large-scale combinatorial optimization. At last, we use multi-process technology to accelerate the operation of the algorithm, so as to complete the algorithm iteration faster. Numerical results show that the proposed methods achieve better performances than three baselines. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 71
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 159211048
- Full Text :
- https://doi.org/10.1109/TVT.2022.3182048