# An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers
## 4 Simulation Experiments and Analyses  
We use Container-CloudSim, whose simulation flows are shown in Figure 7, which is the container cloud evaluation environment in CloudSim [13], to simulate the designed microservice resource scheduling optimization model and algorithm. In order to compare the effectiveness of the algorithm, we select the round-robin algorithm (RR) [23], the most-utilization first algorithm (MF) [24], and the first come first serve (FCFS) algorithm in ContainerCloudSim-4.0 for comparison. The experiments were run on Intel(r) Core(tm) i5-4200U [EMAIL_ADDRESS] GHz and 8G RAM computer. To simulate servers' situation in edge computing, we set all servers with the same performance, as shown in Table 1. As stated in the previous chapter, we mainly consider four types of resource utilization: CPU, memory, disk, and bandwidth. We set the microservice container's size to be tenth order, hundredth order, and thousandth order to better simulate the deployment scenario of the V2X application deployment, as shown in Table 2. The characteristics and calling dependencies of containers are created by random functions. These three orders of magnitude can correspond to small-scale, medium-scale, and large-scale deployment scenarios of V2X applications. We found that when \(\alpha=0.3\), \(\beta=0.2\), and \(\lambda=0.5\), the simulation could achieve the best results through multiple sets of experiments. Compared with the other three algorithms, MFGA consumed the fewest physical hosts when the amount of microservice containers reached 280 or more in Figure 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **Core of CPU** & **MIPS** & **RAM (MB)** & **ROM (MB)** & **Bandwidth (MBps)** \\ \hline server & 4 & 3000 & 4096 & 1,000,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical server configuration information. Figure 7: The flow of Container-CloudSim simulation. Significantly, the multiple fitness genetic algorithm (MAGA) used 20% fewer hosts than the FCFS and RR algorithms, when the size of containers reached the thousandth order. Figure 9 shows the simulation result of the resource utilization rate of the data center. Similarly, the advantages of MFGA become more and more significant in resource utilization with the increase in the microservice containers. The MFGA algorithm keeps the resource utilization of 0.8 in the hundredth or thousandth order scenario, which is superior to all the other algorithms. Figure 10 shows the simulation results of four algorithms in terms of calling distance. The MFGA has a smaller calling distance, which means that MFGA needs less cross-server scheduling when deploying containers. Although the number of containers changed drastically, the growth curve of the MFGA was not noticeable compared to the other three algorithms. In other words, the MFGA could  
Figure 8: Comparison of the number of hosts occupied compared by four algorithms. Figure 9: Comparison of the resource utilization rate of data center compared with four algorithms. perform better in the scenario of a large number of containers due to the consideration of the container's dependencies, which was an important reason that MFGA performed better in the resource utilization. Figure 11 shows the comparison of four algorithms in terms of the resource utilization imbalance. The resource utilization imbalance increased as the number of containers grew. The MFGA maintained a relatively low level in resource utilization imbalance compared to other algorithms, which allowed it to achieve an ideal resource balance effect. In all, we have achieved our optimization goals: fewest hosts occupied, most resource utilization, lowest calling distance, and resource utilization imbalance. In particular, the MFGA performed well in the thousandth order scenario, which is similar to the real situation of deploying V2X containers. Thus, MFGA is an efficient choice to solve resource scheduling problems. Figure 11: Comparison of resource utilization imbalance compared with four algorithms.