# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 4 Validation and Result Analysis
### KPIs with Different Numbers of OVSs  
Referring to the input data in Reference [33] in our experiments, we randomly input 50-400 OVSs in the virtual network slicing and output the network run time, throughput and average delays. The other simulation parameters are shown in Table 2. Figure 5 shows the comparison results of network run time under different numbers of OVSs in virtual network slicing. We can see from Figure 5 that the network runs for the longest time when there are 250 OVSs in the network. This is because when there are fewer OVSs and the transmission distance increases, OVSs will consume the energy resources very rapidly. Meanwhile, when there are more OVSs and the network generates more data, each link needs to bears more data traffic transmission tasks. Both of the above cases will lead to the excessive energy resource consumption of OVSs. From the point of view of OPEX, when there are 250 OVSs, the virtual network has the optimal performance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline The number of virtual OVSs & 50\(\sim\)400 \\ Radius (m) & 100 \\ Data packet size (KB) & 1024 \\ Channel bandwidth (MB) & 20 \\ Duration of OVS monitoring data (ms) & \(1\) \\ Link latency (s) & \(1\times\text{e}^{-5}\) \\ Traffic degradation ratio \(d\) & 0.3 \\ OVS initial energy resources (J) & 600 \\ SNR (dm) & 1\(\sim\)30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 4: The peak rates under different SNRs. Nonetheless, the proposed TME algorithm always has the longest network run time compared with the HPR, TE and A* Search algorithms. In addition, the maximum value of running time can be reached with 250 OVSs, and the network running time of the proposed TME algorithm can be up to 206 rounds, while of the HPR, TE and A* Search algorithms are 127, 100 and 124, respectively. Obviously, the performance of the proposed TME algorithm is almost improved double that of the other three algorithms. This is because the TME algorithm migrates the links if the predicted traffic exceeds the peak rates, and thus balances the local traffic load and reduces the consumption of energy resources, as well. It is an iterative process from local optimization to global optimization. This process effectively inhibits the excessive energy consumption of OVSs caused by traffic overload of local links and prolongs the service time of all OVSs in the whole virtual network at the same time. Figure 6 shows the network throughput with different numbers of OVSs in the virtual network. We can see from Figure 6 that the network throughput increases gradually as the numbers of OVS increase. This is because each OVS is responsible for monitoring and transmitting the traffic data, and the traffic increases as the numbers of OVSs increase. Regardless, the network throughput of the proposed TME algorithm is always more than that of the HPR, TE and A* Search algorithms as the number of OVSs increases. The TME controls the loss of traffic and improves the throughput of the network effectively. From the point of view of the network local link, the overloads of data traffic for the HPR, TE and A* Search algorithms can lead to data losses, thus reducing the network throughput. Figure 7 shows the network average delays with different numbers of OVSs in virtual network and the inconsistency of the average delays as the numbers of OVSs increase. We can see from Figure 7 that the minimum delays occur with 250 OVSs in the network for all algorithms, and the reasons are the same as the network run time. The traffic transmission distance will be longer if the number of OVSs is smaller, and the OVS needs much more time to transmit the traffic. The more OVSs there are, the more data traffic needs to be transmitted, which will lead to an increase of time delay. From the point of view of the delay of 5G network, the optimal number of equipment supported by one MBS within 1 km\({}^{2}\) in the physical layer is 200-300 in our simulation environment.