## 4 Deployment of Cloud-Native Core Network in ComEdge
### Evaluation Results  
The ComEdge platform's main experimental environment consists of five edge nodes as shown in Table 1. The available resource units for edge nodes are set to the minimum memory value occupied by all CNFs. The main observations in the experiments focus on the two core network processes introduced in Section 4.1: registration and session establishment. Based on Alibaba Cloud's unit costs for CNF's operation and control traffic [29], \(D_{OP}\) and \(D_{CM}\) are, respectively, set to 0.875 RMB per hour and 0.233 RMB per hour. In addition, we implemented DQN-CNFDA using PyTorch and conducted replays on the ComEdge platform with real data from different cells [30]. The experiments compared the differences in cost and resource utilization among the following edge core network deployment schemes: (1) DQN-CNFDA described in Section 4; (2) the minimum deployment cost scheme (MDCS) mentioned in [31]; (3) engineering-experience-based CNF deployment scheme (ENE-CNFDS); (4) random CNF deployment scheme (R-CNFDS). Among these, Scheme 2 represents a category of current edge core network deployments (as described in [31]), which primarily focus on minimizing the deployment cost of network functions while not paying sufficient attention to control traffic overhead. Scheme 3 represents the approach described in [17; 18; 19], where either the entire or a portion of core network functions are statically deployed at satellite edge nodes. This approach solely addresses the significant control traffic overhead issue caused by frequent user-triggered registration process handovers in the ground core network, while neglecting the critical factor of deployment costs in the edge core network. The experimental results of the four aforementioned schemes in the ComEdge platform are presented in Figure 6.  
Figure 6: The results of the experiments conducted on the ComEdge platform. Figure 6a illustrates a comparison of platform resource usage for different schemes as the number of users in the wireless coverage area of edge nodes increases. We use the more intuitive platform average memory usage. Consistent with subjective inference, when the number of users within the coverage of edge nodes increases, the number of running containers in the entire platform also increases accordingly, leading to an increase in platform memory usage. R-CNFDS neglects the control traffic overhead between nodes, resulting in inefficient resource utilization. This situation becomes even more severe as the number of users increases. It is worth noting that when the number of users is small, there is little difference in resource utilization between MDCS, ENE-CNFDS, and DQN-CNFDA. With fewer user requests within the node coverage, the required number of containers to be deployed is limited, resulting in relatively good performance for ENE-CNFDS, even though it does not consider deployment costs as effectively. Additionally, the lower volume of control signaling between nodes does not significantly impact MDCS in terms of control traffic. However, as the number of users increases (e.g., when a single node covers 70 to 100 users), DQN-CNFDA outperforms MDCS and ENE-CNFDS in effectively managing the relationship between deployment costs, control traffic, and dynamic user requests. It efficiently scales network functions on each node, leading to more pronounced advantages in system resource utilization. Figure 6b illustrates a comparison of the costs associated with distributed deployment and operation of the core network on edge nodes using various schemes. All the required CNFs are launched simultaneously at 7:00, resulting in equal costs for all four schemes at the begin time. The operational costs of the edge core network increase gradually as the number of edge users and time progresses. The number of edge users gradually increases over time, leading to a corresponding increase in the operating cost of the edge core network. From 7:00 to 18:00, DQN-CNFDA successfully minimized the costs.