# An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers
## 1 Introduction  
Connected and autonomous vehicle (CAV) technology involves the application of the Internet of Things (IoT) technology in intelligent transportation. CAV has become a vital component of the intelligent transportation system with the rapid deployment of 5G networks [1]. In the future, vehicle-to-everything (V2X) communications will become a reality [2], which consists of vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-network (V2N). Through V2X, we can break through the technical bottlenecks of vehicle non-line-of-sight perception and information sharing. As a result, various CAV applications have also been spawned, such as platooning, advanced driving, and remote driving. However, the delay response, computing speed, and bandwidth of these application services have a high demand for service requirements. For example, the allowable delay for remote driving is 5-10 ms [3], undoubtedly increasing the communication burden. In this study, we try to propose a resource scheduling strategy to improve the utilization, which aims to meet the V2X service demand. The combination of edge cloud computing and V2X technology has become a new solution to satisfy the above demand, as shown in Figure 1. The edge cloud is a comprehensive elastic cloud platform with computing, storage, network, and security capabilities [4]. Edge cloud computing has six advantages: low latency, self-organization, definable, schedulable, high security, and open standards. The most significant benefits are the unified coordination and service capabilities, which will effectively reduce response time, reduce bandwidth, and improve service quality when deploying the V2X applications on the edge cloud. However, the resource capacity of edge computing servers is less than the traditional cloud computing servers [5]. Thus, it is crucial to make full use of the edge cloud hardware resources within the limit of resource conditions to ensure the high reliability, flexibility, and high performance of the V2X applications. Microservice consists of a series of software components with interdependence and specific functions, which is significant for the full use of resources [6]. Based on microservice, each application can be independently deployed, extended, and updated, which will reduce the development cycle and increase flexibility. However, microservices were deployed directly on bare metal before the advent of container technology, which was challenging to operate and maintain [7]. Moreover, the container is a lightweight virtualization technology [8], whose engine layer provides resource scheduling and component isolation for microservices, as shown in Figure 2. Containers directly use hardware resources in the physical host, which can meet different platforms' requirements regarding development, test, and production environments [9]. However, the granularity of containers is smaller, and the number of physical machines that can be operated is higher, which means that resource management for containers is more complicated. In this study, we will develop a resource scheduling strategy to improve the performance of the container-based microservice. Figure 1: V2X applications deployed in edge computing. Figure 2: Schematic diagram of cloud microservice resource scheduling. At present, standard container orchestration tools such as Kubernetes and Docker SwarmKit can only provide simple resource scheduling strategies [10] that cannot utilize physical machine resources. Thus, some studies proposed strategies for better resource utilization. For example, [PERSON] [11] proposed an edge computing architecture that utilized container-based microservice to ensure the flexible deployment of applications in 2016. [PERSON] et al. [12] proposed a cloud center infrastructure which was based on the container virtualization technology. They also put forward a resource scheduling method of container virtual cloud environments to reduce customer operation time and improve supplier resource utilization.