## 1 Introduction
We further analyze the performance of the proposed routing framework against routing in VANETs using Ad Hoc On-demand Distance Vector (AODV) and routing in SDVN, making use of the Dijkstra shortest path algorithm. The proposed hybrid routing framework is novel in various aspects. Firstly, we are the first to identify the factors and model exact one-hop channel delay estimation using a machine learning approach for heterogeneous communication channels with the help of network metadata collected by the controller in SDVN. Existing works fail to compute an exact one-hop channel delay, as there exists only a theoretical approach to estimating contention delay in the existing literature. Secondly, the hybrid routing algorithm (Figure 1) is novel in its approach, in that it selects its mode by examining the current network state in the form of normalized network contention, where both modes select the highest stable links. The least distance mode under high network contention selects stable shortest paths, while the least delay mode under low network contention selects stable least delay paths. This hybrid approach has not yet been investigated by any researcher, to the best of our knowledge. Thirdly, the routing framework proposes a novel flow table update algorithm (Figure 2) and an adaptive flow rule computation approach (Figure 3), along with a modified flow table architecture by adding a route-valid timestamp to the OpenFlow flow table structure to update the controller's flow table. Flow rule computation is adaptive in its approach, which is different from the proactive and reactive approaches found in the existing literature for flow table updating. Finally, a novel systematic approach for the whole routing process, including metadata collection, parameter computations, the flow table update, and unicasting to switches, is presented. Contributions to the existing literature from this research are listed below:  
* We present a novel hybrid routing algorithm for SDVNs that uses collected metadata from nodes to estimate parameters at the controller to compute routes that yield the least communication cost, the highest packet delivery ratio, the least latency, and moderate channel utilization, on average, compared to routing in VANETs (AODV) and SDVNs (Dijkstra);
* This research provides insight into the employment of machine learning for accurate and computationally efficient parameter computation at the controller, such as the estimation of link delay and lifetime, satisfying the communication requirements of vehicular networks;
* This research experimentally investigates the factors under which the proposed hybrid routing framework is enhanced, so that these factors can be effectively used by future researchers to boost the performance of the proposed novel hybrid routing framework;  
Figure 1: Pseudo-code for finding the highest stable shortest/least delay paths to a given destination node. * The proposed routing algorithm will be very useful for more efficient dissemination of information in the data plane of future SDVNs than existing approaches. This routing framework is limited to SDVNs, in which the control plane collects metadata from all vehicle nodes in a logically centralized manner to generate matrices such as link lifetime and link delay, with the help of machine learning and compute parameters such as normalized network contention, in order to compute routes using the proposed algorithm. For instance, the proposed algorithm cannot be applied to a VANET where there is no logically centralized control plane that collects the metadata required to execute the proposed routing algorithm. The following is an outline of the remainder of the paper. The background on VANETs, SDVNs and their current architectures, routing in SDVN, and the application of machine learning for routing are covered in Section 2's assessment of the literature. The proposed routing framework is described in depth in Section 3.