# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
## 5 Numerical Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Virtual Functions** & \(\boldsymbol{\phi}\) & \(\boldsymbol{\xi}\) & \\ \hline Load Balancer & 0.40\% & 0.0012\% & \\ Deep Packet Inspector & 0.90\% & 0.0011\% & \\ Acquirer & 0.12\% & 0.0013\% & \\ Edge Streamer & 0.20\% & 0.0015\% & \\ Point-to-Multipoint & 0.12\% & 0.0013\% & \\ \hline  & & Q1 & Q2 & Q3 \\ Transcoder (Q1–Q6) & 0.11\% & 0.0050\% & 0.0064\% & 0.0078\% \\  & & Q4 & Q5 & Q6 \\  & & 0.0092\% & 0.0106\% & 0.0120\% \\ \hline Transcoder (low quality) & 0.11\% & & 0.005\% & \\ Storage & 0.12\% & & 0.03\% & \\ Flow Rebuilder & 0.12\% & & 0.08\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Load parameters of the virtual functions, measured experimentally. number of video transmitters. Moreover, we can observe that the PM block is the most CPU-hungry in the acquirers, while it occupies the least portion of CPU in the edge streamers. The first reason is due to the high number of edge streamers, each requiring a copy of each flow, copies that have to be created in the acquirers. The second reason is related to the presence of the transcoding function, which is mostly put on the acquirers because, for the majority of the flows, there are clients requesting the same quality level and attached to different edge streamers; in this case, the transcoders are put on the root of the tree, that is, on the acquirer. The model proposed in the paper can be used by the orchestrator to evaluate, and then optimize, the number of computation units to activate on each drone. For this reason, we have presented in Figure 7 the mean value of the number of active computation units needed in each drone. In order to show the total information that is available to the orchestrator for its decisions, we have analyzed a specific situation where the rural area is covered by 200 video transmitters, i.e., 50 fixed cameras and 150 video transmitter drones, connected to the backbone mesh network with a fixed topology. Figure 8 shows the detailed computational load in the acquirers and in the edge streamers, while Figure 9 summarizes the number of needed computation units in each acquirer and in each edge streamer to support the load, taking into account that each computation units can support at most a load of 100%. Using this information, the orchestrator can decide to switch off some backbone drones, or switch on some of them to migrate on it some virtual function in order to decrease the load of other drones that need to save some energy to extend their flight life. Figure 6: Average computational load. (**a**) acquirer blocks; (**b**) edge streamer blocks. LB: load balancer; DPI: deep packet inspector; ES: edge streamer; PM: point-to-multipoint; TC: Transcoder. Figure 7: Computation units needed per drone. Finally, to this purpose Figure 10 presents the mean number of active computation units needed in a single backbone drone by varying the number of acquirer nodes in the network, and for different numbers of video transmitters. Figure 8: Per-block computational load. (**a**) computational load of each block in each acquirer; (**b**) computational load of each block in each edge streamer. TC: transcoder. Figure 9: Number of needed computational units per drone.