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Dynamic Controller Assignment in Software Defined Internet of Vehicles through Multi-Agent Deep Reinforcement Learning

Abstract : 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.
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https://hal.inria.fr/hal-03000911
Contributor : Thierry Turletti <>
Submitted on : Thursday, December 17, 2020 - 1:16:24 PM
Last modification on : Monday, January 4, 2021 - 6:26:23 PM

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Tingting Yuan, Wilson da Rocha Neto, Christian Rothenberg, Katia Obraczka, Chadi Barakat, et al.. Dynamic Controller Assignment in Software Defined Internet of Vehicles through Multi-Agent Deep Reinforcement Learning. IEEE Transactions on Network and Service Management, IEEE, In press, ⟨10.1109/TNSM.2020.3047765⟩. ⟨hal-03000911v2⟩

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