# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 4 Simulation Results
The results are shown in Figure 8, where the green bar indicates the IPPO-RF algorithm and the blue bar indicates the IPPO-DFS algorithm. It can be seen that the two algorithms obtain the approximately same profit, but the run time using RF is reduced by 198% compared to using DFS. Therefore, the RF algorithm is more efficient than the DFS algorithm in the sub-problem of verifying the feasibility of VNF mapping. This is precisely the significance of introducing machine learning into our algorithm. Next, we illustrate the case where the PRU is relatively large and/or the amount of resources is relatively limited, i.e., not all users can have their PRU met. We run the proposed IPPO-RF algorithm and plot the user satisfaction of the eMBB, URLLC, and NPN slices for five different instances of personalized rate requirements in Figure 9. As can be seen, the user satisfaction of the NPN slice is always 1, which means that the higher service priority of the NPN users are guaranteed via the proposed IPPO-RF algorithm. Due to the limited resources, the user satisfaction of the eMBB slice and the URLLC slice is strictly below 1 but higher than the user satisfaction threshold, i.e., \(\zeta_{min}^{e}=\zeta_{min}^{u}=0.9\). Whether the eMBB slice or the URLLC slice should obtain a larger user satisfaction depends on the RB allocation scheme that can maximize the profit, and, at the same time, be implementable by a VNF mapping, and such an RB allocation scheme is found using the IPPO-RF algorithm. Figure 8: Comparison of the IPPO-RF and IPPO-DFS. Figure 7: Training rewards for IPPO. (**a**) Actual reward curve during training. (**b**) Smoothed reward curve with moving average processing. It can be concluded that the resource allocation scheme found via the proposed IPPO-RF algorithm does reflect that the NPN slice has higher service priority and reliability compared to the eMBB and uRLLC slices. Next, we demonstrate that the RB allocation scheme found using the proposed IPPO-RF scheme can indeed guarantee the security and privacy of the NPN slices. First, we introduce three performance metrics related to the privacy of a slice: (1) the privacy of a slice on the servers; (2) the privacy of a slice on the links; and (3) the average privacy of a slice. We primarily characterize the privacy of each slice by the extent of the sharing of the servers and links occupied by the VNFs of each slice. The privacy of a slice on the servers depicts how heavily the slice shares the server nodes with other slices. More specifically, we count how many slices occupy the same server node occupied by the \(j\)-th VNF of Slice \(s\) and denote this number as \(n_{j}^{s}\). The privacy of Slice \(s\) on the servers is defined as \(\sum_{j=1}^{t_{s}}\frac{c_{j}^{s}}{c_{j}^{s}}\), where \(c_{j}^{s}\) denotes the computing resources required for the \(j\)-th VNF of slice \(s\), and \(c^{s}=\sum_{j=1}^{t_{s}}c_{j}^{s}\). Similarly, we define the privacy of Slice \(s\) on the links. Finally, the average privacy of a slice is the average of the privacy of a slice on the servers and the links. In Figure 10, we demonstrate the privacy of the eMBB, URLLC, and NPN slices obtained via the proposed IPPO-RF algorithm for one instance of personalized user requirements. It can be seen that the privacy of the NPN slice on the servers and links are both 1, which means that the VNFs in the NPN slice do not share server nodes or links with other slices, thus improving its security and privacy. On the other hand, the eMBB slice and the URLLC slice share some servers and links to maximize the profit. It can be concluded that the resource allocation scheme found using the proposed IPPO-RF algorithm indeed indicates that the NPN slice has higher security and privacy compared to the eMBB and uRLLC slices. Finally, we illustrate the performance gain of the proposed IPPO-RF algorithm with three benchmark methods.