Dynamic Controller Assignment in Software Defined Internet of Vehicles through Multi-Agent Deep Reinforcement Learning
- Others:
- Design, Implementation and Analysis of Networking Architectures (DIANA) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- Institute of Computing [Campinas] (IC) ; Universidade Estadual de Campinas = University of Campinas (UNICAMP)
- Jack Baskin School of Engineering (UCSC) ; University of California [Santa Cruz] (UC Santa Cruz) ; University of California (UC)-University of California (UC)
- This work was partly funded by Inria, supported by the French ANR "Investments for the Future" Program reference #ANR-11-LABX-0031-01, and UNICAMP, through the FAPESP Grant number #2017/50361-0, both in the context of the DrIVE #EQA-041801 associated team.
- ANR-11-LABX-0031,UCN@SOPHIA,Réseau orienté utilisateur(2011)
Description
In this paper, we introduce a novel dynamic controller assignment algorithm targeting connected vehicle services and applications, also known as Internet of Vehicles (IoV). The proposed approach considers a hierarchically distributed control plane, decoupled from the data plane, and uses vehicle location and control traffic load to perform controller assignment dynamically. We model the dynamic controller assignment problem as a multi-agent Markov game and solve it with cooperative multi-agent deep reinforcement learning. Simulation results using real-world vehicle mobility traces show that the proposed approach outperforms existing ones by reducing control delay as well as packet loss. Index Terms-Internet of Vehicles (IoV), Software Defined Networking (SDN), multi-agent deep reinforcement learning, controller assignment.
Abstract
International audience
Additional details
- URL
- https://hal.inria.fr/hal-03000911
- URN
- urn:oai:HAL:hal-03000911v2
- Origin repository
- UNICA