Opti-Route: A Machine Learning-Based Scheme for Optimal Placement and Selection of Controllers With an Optimal Route in Software-Defined Vehicular Network

The controller placement problem and controller selection problem are two major issues that, if not considered seriously, lead to high network costs, load unbalancing, and high latency. To ensure stable connection in software-defined vehicular network (SDVN) with low cost, balanced load, and less latency, an optimal solution must be found that tackles all issues simultaneously. The aim of this article is to propose a solution called Opti-Route. Opti-Route focuses on solving three major problems simultaneously for SDVN. The cost of installing controllers at almost every roadside unit (RSU) to ensure stable connections is high. The second problem lies in the imbalance of load caused by the presence of a single main controller (MC) and multiple local controllers (LCs), and the third problem is the high latency involved in creating an end-to-end path from source to destination in a highly dynamic network. Opti-Route tackles these challenges by first analyzing the optimal placement of controllers from among all the RSUs in the network using a support vector machine (SVM) algorithm. From these selected LCs, it then selects the optimal controller (OC) to share the burden of the MC at the location where traffic density is high. And then, finally, taking instructions from optimal and local controllers, Opti-Route selects an optimal network route that ensures a high delivery ratio with minimum delay and overhead. To demonstrate the effectiveness of SVM in a dynamic SDVN architecture, we tested our Opti-Route in various case scenarios and compared it with different learning algorithms. Our result shows a minimum delay of 0.04 ms, a high delivery ratio of 97%, and a minimum overhead of 4%.