## 4 Simulation Results and Discussions
### Results and Discussions
#### 4.3.2 Evaluation Result of the Delay-Aware on the Adaptive Slicing System  
In the second experiment, we examine the impact of the delay-aware ASA (DA-ASA) system, which adds a maximum tolerable delay \(\left(MTD_{i}\right)\) factor to the ASP Equation (17) for each slice. We use the ASA system as a reference to compare it with the DA-ASA system. As previously stated, the URLLC slice has the highest priority, the eMBB has a medium priority, and the mMTC slice has the lowest priority. The ASA system adaptively adjusts the priority of each slice by applying the ASP Equation (16). Moreover, in the DA-ASA system, the priority of each slice is adaptively modified using the ASP Equation (17). We exploit the parameters listed in Tables 9 and 10 unless otherwise specified. We display the performance measures of each slice as a function of total system's traffic loads. Then, we investigate the similarities and differences between DA-ASA and the ASA. Figure 14 shows the total system utilization of both systems. DA-ASA and ASA achieve full system utilization due to similarities in their functions. Figure 15 shows the resource utilization of the network slices. The DA-ASA slightly increases the utilization of the URLLC compared to the ASA. In addition, since the mMTC slice has the lowest priority, it has a small portion of the available resources. When the mMTC requests wait longer to be served, the \(MTD_{i}\) parameter of mMTC in the ASP equation will increase its priority, which will allow it more access to resources. Therefore, the DA-ASA increases the utilization of the mMTC compared to the ASA, particularly at high traffic loads (i.e., above 160 Erl). However, the improvement of the mMTC utilization in DA-ASA comes at the expense of a slight decrease in eMBB utilization in comparison to ASA. Figure 16 illustrates the average waiting time of both systems. At low-to-moderate loads (i.e., 50-250 Erl), the average waiting time of the DA-ASA is almost equal to the ASA. However, at high traffic loads (i.e., above 260 Erl), the average waiting time of the DA-ASA is slightly higher than that of the ASA due to the increase in the average waiting time of the eMBB. Figure 14: Total system utilization vs. total offered traffic loads (Experiment 2). Figure 17 shows the average waiting time of the network slices in both systems. the average waiting time of the URLLC is the same in both systems. In addition, we notice that the average waiting time of the mMTC in DA-ASA is lower than the ASA. As the traffic load increases, the requests of the mMTC slice, which has the lowest priority, must wait longer for services since there are limited resources. The ASP value of the mMTC in DA-ASA decreases as the difference between the \(MTD_{i}\) and the current delay of mMTC request decreases. Therefore, in DA-ASA, when the ASP value of the mMTC request decreases, its priority in obtaining the service increases. However, the eMBB in the DA-ASA has a longer average waiting time compared to the ASA due to the increase in resource consumption from the mMTC. Figure 16: Average waiting time of system vs. total offered traffic loads (Experiment 2). Figure 15: Utilization of network slices vs. total offered traffic loads (Experiment 2). Figure 18 illustrates the TBP of both systems. The DA-ASA achieves a slight decrease in the TBP compared to the ASA. Figure 19 shows the TBP of the network slices in both systems. The DA-ASA slightly lowers the TBP of the URLLC in comparison to the ASA, particularly at high traffic loads. The DA-ASA increase the mMTC priority, which enables more admission of its requests. Therefore, The DA-ASA lower the TBP of mMTC compared to the ASA. Consequently, The DA-ASA slightly increase the TBP of the eMBB in comparison to the ASA. We can conclude that the DA-ASA improves the performance of the mMTC, which is the lowest priority slice by increasing its chance in obtaining service.