## 5 Experimentation
### Results
#### 5.5.4 Evaluation of the Control Settings Impact on the Delivery Ratio  
The control layer is configurable through the settings listed in Section 5.4.2. We have run simulations over the four selected representative scenarios to analyze the impact of the Control configuration's settings on the delivery ratio over diverse scenarios and to find a general configuration that fits all of them. The following nine sections present our analysis. Figure 10: Delivery ratio percentage for the different forwarding policies per scenario. Figure 12 shows the delivery ratio depending on the number of controllers used per scenario. In this plot, the max number of considered controllers is 50. Not all the scenarios have been simulated for all the considered number of controllers as, for example, the Info5 scenario has just 41 nodes. Certainly, as pointed out in Section 3.4, the number of controllers needed to orchestrate the OppNet depends on the nature of the network. In addition, the characteristics of the four simulated scenarios, including the number of nodes, are very different. Therefore, for each scenario, we terminated the simulation as soon as the results showed a clear descending slope corresponding to an increasing number of controllers. Specifically, Figure 12 shows that for all the scenarios, a small number of controllers perform better in terms of delivery ratio. For the most connected scenarios, Campus and Info5, the best performance is achieved with just four and two controllers, respectively. As the controller receives the congestion readings from the nearby nodes, it obtains an overview of the congestion of a part of the network, its nearest part. We can elaborate on this idea by considering that the network is \"segmented\" by the number of controllers used. Each network \"segment\" consists of the number of nodes that can be reached by a controller directly or through short time relays. Having said that, in a highly connected network, using a high number of controllers results in an overlap of the different network segments, as each controller can reach several of these segments. This overlapping effect results in the nodes receiving directives from different controllers. Of course, a directive emitted from a controller from a segment the node does not belong to has congestion information that is not entirely accurate for the node. This overlapping effect is why using many controllers decreases the network performance. On the other hand, for the sparser scenario (Taxis) and the scenario with an abrupt change in the communication conditions (Emergency), the implicit segmentation derived by the different controllers obtains disjointed segments. Under these circumstances, having  
Figure 11: Latency average for the different forwarding policies per scenario. a higher number of controllers (10 for Taxis and 20 for Emergency) helps cover a broader network range, translating to better performance. Optimal Congestion Interval (\(o_{min}-o_{max}\))  
As presented in Section 4.4, the current replication limit is not modified when the congestion calculated by a controller falls in the optimal congestion interval. Figure 13 shows that for the most congested scenario (Campus), there is a similar delivery ratio for all the optimal congestion intervals. The variance between the performance results for the different intervals is just 0.0002. Nevertheless, we obtain the best result for the interval 0.5-0.8. These homogeneous results infer that in a congested scenario, if the congestion predictions fit in the configured optimal congestion interval setting for the current replication limit, the best strategy is to keep the current replication limit steady. The medium- to high-congestion scenario (Info5) shows slightly more variance in the delivery ratio than the previous scenario (0.001). This variance can be appreciated mainly when the interval upper bound (\(o_{max}\)) is 0.8 and 0.9.