# An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers
## 1 Introduction
[PERSON] et al. [13] proposed a new microservice scheduling model in a heterogeneous cloud edge environment, whose novelty was the microservices-oriented method. [PERSON] et al. [14] proposed a container scheduling strategy based on the ant colony algorithm to solve the shortcomings of the Docker SwarmKit orchestration engine's scheduling strategy. The above strategy can improve the utilization to a certain extent, but it lacks the consideration of the multi-objective in the scheduling process. There were some studies considering the multi-objective problem. For example, [PERSON] et al. [15] designed a multi-objective function using a game theory model to reduce energy consumption and maximum task completion time by considering different constraints such as memory, CPU, and user budget. Meanwhile, they used lightweight containers instead of traditional virtual machines to reduce the fog device's overhead, response time and overall energy consumption. [PERSON] et al. [16] proposed a fuzzy service offload decision mechanism (FSODM) algorithm based on the characteristics of limited resources under edge computing and based on the micro-scale deployment architecture to achieve rapid deployment and dynamic expansion of microservice containers. [PERSON] and [PERSON] [17] proposed an evolutionary memetic algorithm to solve the multi-objective container optimization problem in a dynamic fog environment, whose goals were Quality of Service (QoS) maximization, survivability factor maximization, and active host minimization. [PERSON] et al. [18] established a multi-objective optimization model for container-based microservice scheduling, which took computing, the storage resources of the physical nodes, the number of microservice requests, and the failure rate of the physical nodes into consideration. The current proposed multi-objective studies lacked the consideration of microservices' independence, which made the improvement of resource utilization and other goals limited. Some studies considering mobile agents have been proposed. For example, [PERSON] et al. [19] used a tail matching subsequence (TMSS) based mobile access prediction method which exploited the inherent user mobility patterns to solve the intermittent connectivity. Meanwhile, the authors developed an integer encoding-based adaptive genetic algorithm (GA) to make offloading decisions, which considered the cloudlet reliability, computation requirements, and mobile device energy consumption. [PERSON] et al. [20] established the vertical and horizontal scaling mechanism to satisfy the QoS requirements. The vertical scaling mechanism used the linear switching system and state feedback controller to develop a dynamic resource allocation and admission control mechanism. The horizontal scaling mechanism was in charge of each edge server's activation/deactivation, the placement of the applications' instances, and the distribution of the incoming offloaded requests. In this study, we try to address several challenges in resource scheduling for V2X microservice deployment. Previous studies did not consider the microservice dependencies, which led to wasted resources. In our study, we try to use the calling distance to quantify the dependence. Then, we model a multi-objective model considering resource utilization, resource balancing, and calling distance. We propose a multiple fitness genetic algorithm to balance the above factors, where a container dynamic migration strategy is introduced in the crossover and mutation operation. The rest of this study is organized as follows. The resource scheduling model in containerized microservice is discussed in Section 2. The resource scheduling algorithm is proposed in Section 3. The simulation experiments will be discussed in Section 4. Finally, we conclude this study in Section 5.