## 1 Introduction  
In the Internet of Vehicles (IOV) environment for intelligent transportation systems (ITSs), the usage rate of audio, video, text messages, and other content has increased, which limits the available bandwidth and resources of other applications. In recent years, as cloud computing and edge computing have become important means to assist ITSs in information transmission [1], ITS applications and use cases have become more diversified and specialized. On-Board Unit (OBU) cloud [2; 3; 4], Road-Side Unit (RSU) cloud [5] and hybrid cloud [6] provide a basic environment for communication protocols, computing infrastructure, services, and applications to improve the efficiency of the IOV. Data center networks and wide area networks are increasingly adopting the Software-Defined Network (SDN) paradigm, which also brings the emergence to use SDN for managing the IOV network. The cloud resource manager is responsible for calculating the network configuration for the change in network requirements from time to time to meet the dynamic characteristics of the network.  
As shown in Figure 1 below, the vehicle can communicate with the RSU cloud. The communication in the cloud is managed by the OpenFlow controller, and its basic computing unit is the virtual machine on the Road-Side Unit server. Considering the QoS requirements, it is necessary to combine computer and link load balancing for cloud resource management. By deploying MEC servers near the available base stations connecting vehicles, SDN cloud resource management provides higher-performance operations for data centers and edge servers. It helps edge computing operate in near-optimal conditions. While obtaining continuous services from edge servers, due to mobility and cloud resource management requirements, virtual machines (VMs) need to switch from one Cloudlet to another Cloudlet. In order to achieve smooth operation in a dynamic environment, SDN needs to be used to reshape the network infrastructure to achieve efficient cloud resource management. As for the SDN, the Controller Placement Problem (CPP) of SDN multi-controller needs to be considered in the cooperative communication of medium- and large-scale SDNs [7; 8]. CPP is an important part of SDN design, and the controller layout depends on the goal of network design. Without loss of generality, for the placement of SDN controllers, the existing literature has the most research on two representative targets: network delay and number of controllers. [PERSON] et al. [9] first proposed to study the multi-controller placement problem in SDNs. They considered the number of controllers, the location of controllers, and the switches assigned to each controller in CPP. [PERSON] et al. [10] used a k-means-based network partitioning algorithm to solve the CPP problem, which aims to minimize the delay of the switch controller. [PERSON] et al. [11] Addressed the challenge of controller deployment in software-defined networking via affinity propagation, where an improved clustering approach inspired by exemplar-based techniques and leveraging affinity propagation is introduced. In the study on switch-controller interactions, [PERSON] et al. [12] highlighted the critical role of inter-controller communication, developing a latency analysis model based on two distinct inter-controller data ownership frameworks. [PERSON] et al. [13] studied CPP to ensure the flow establishment time. It is assumed that the control load on each switch obeys Poisson distribution, and the service time on each controller obeys exponential distribution.