Intelligent and Resizable Control Plane for Software Defined Vehicular Network : A Deep Reinforcement Learning Approach

Abstract

Software-Defined Networking (SDN) has become one of the most promising paradigms to manage large scale networks. Distributing the SDN Control proved its performance in terms of resiliency and scalability. However, the choice of the number of controllers to use remains problematic. A large number of controllers may be oversized inducing an overhead in the investment cost and the synchronization cost in terms of delay and traffic load. However, a small number of controllers may be insufficient to achieve the objective of the distributed approach. So, the number of used controllers should be tuned in function of the traffic charge and application requirements. In this paper, we present an Intelligent and Resizable Control Plane for Software Defined Vehicular Network architecture (IRCP-SDVN), where SDN capabilities coupled with Deep Reinforcement Learning (DRL) allow achieving better QoS for Vehicular Applications. Interacting with SDVN, DRL agent decides the optimal number of distributed controllers to deploy according to the network environment (number of vehicles, load, speed etc.). To the best of our knowledge, this is the first work that adjusts the number of controllers by learning from the vehicular environment dynamicity. Experimental results proved that our proposed system outperforms static distributed SDVN architecture in terms of end-to-end delay and packet loss.

An Efficient Method for Optimal Allocation of Resources in LPWAN Using Hybrid Coati-Energy Valley Optimization Algorithm Based on Reinforcement Learning

“…The subsequent solution candidate is developed for updating the position, and it is given in Eq. 16 .…”

“…The selection issue in space with high dimensions and space of states is primarily solved by the Deep reinforcement learning algorithm [15]. Artificially formed neural networks are mostly used in the deep learning method to handle high-dimensional issues in decision-making [16]. The key objective of reinforcement learning is to employ an agent to know about the decisionmaking protocol to communicate with its surroundings and receive the most beneficial reward [17].…”