# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 5 Performance Evaluation
### Evaluation Results and Analysis  
To evaluate the experimental results, we compared the state-of-the-art algorithms, as listed in Table 3. The RT-CSP+ and RT-CSP algorithms are our proposed algorithms. First, we evaluated the performances of these algorithms in the scenario where the slice request arrival rate is four requests per 100 time units. Next, we changed the slice link connected probability to study its effects on the performance of the algorithms. Then, to verify the scalability of our algorithms, we examined simulation scenarios with different slice arrival rates and different sizes of the substrate network. We ran each experiment for 10 times to analyze experimental results. 2.1 Experiments in the Scenario where the Slice Request Arrival Rate Is Four Requests Per 100 Time Units  
In this scenario, there are 100 substrate nodes in the substrate network and the slice nodes are connected by a probability of 0.5. The results of slice acceptance ratio, long-term average revenue and the revenue-to-cost ratio of the algorithms are shown in Figures 2 and 3a,b, respectively. Figure 2 shows our algorithm RT-CSP+ has the best slice acceptance performance over the entire simulation time. The acceptance ratio of all algorithms is relatively high at the beginning of the simulation because the CPU and bandwidth of the physical network are sufficient. As the simulation progresses, the available resources of the physical network gradually reduce due to the occupation of the active slice requests in the provisioning system, resulting in a gradual decrease in the slice reception ratio. After 10,000 time units, the slice acceptance ratio tends to stabilize. The reason is that the arrival and departure of the slices reach a relatively balanced state, and thus the available resources of the physical network are relatively stable. When the simulation time reaches 40,000 time units, the slice acceptance ratio of RT-CSP+ is 91.52%, which is 15.06%, 17.93%, and 51.25% higher than those of VNE-DCC, NRM-VNE, and CC, respectively. Our algorithm can comprehensively evaluate nodes from the perspective of local and global resource and topology attributes, making node provisioning more optimized. Thus, our algorithm can increase the slice acceptance ratio. The slice acceptance \begin{table}
\begin{tabular}{c l} \hline \hline
**Parameter** & **Description** \\ \hline Number of substrate nodes & \(50/100/150\) \\ Probability of connecting substrate nodes & \(0.5\) \\ Substrate node CPU & \(U[50,100]\) \\ Substrate link bandwidth & \(U[50,100]\) \\ \hline Lifetime of slice requests obeying & \(500\) time units in average \\ Exponential distribution & \\ Number of slice requests & \(2000\) \\ Number of slice nodes in each slice & \(U[2,10]\) \\ Probability of connecting slice nodes & \(0.2/0.5/0.8\) \\ Slice node CPU demand & \(U[1,20]\) \\ Slice link bandwidth demand & \(U[1,20]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notations of system model. \begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline RT-CSP+ & The provisioning algorithm considering resource and topology attributes with the _minMaxBWUtilHops_ based slice link provisioning \\ RT-CSP & The provisioning algorithm considering resource and topology attributes with the basic \(k\)-shortest path-based slice link provisioning \\ VNE-DCC & The algorithm considering local resource and topology attributes in [33] \\ NRM-VNE & The algorithm only considering local resource attributes in [28] \\ CC & The provisioning algorithm in [21] considering classic closeness centrality \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithms for comparison. ratio of RT-CSP+ is higher than RT-CSP, which shows that our _minMaxBWUtilHops_ strategy in the link provisioning stage can further enhance the performance of RT-CSP. As shown in Figure 3a, the RT-CSP+ algorithm has the largest long-term average slice provisioning revenue. In the early stage of the simulation, the long-term average revenue decreases rapidly.