## 6 Experimental Evaluation
### Experiment II: Service Allocation
#### 6.2.5 Service Allocation Results
Figure 9: Mean user delay (in milliseconds): the ratio between the sum of the delays of all user requests and the total number of requests, varying the number of users in each scenario. Figure 14: User request success rate: percentage ratio between successfully answered user requests and the total number of user requests during the simulation in each scenario. Figure 12: Consumption of CPU resources: percentage of the total CPU consumption for five algorithms, varying the number of users in each scenario. Figure 13: Memory usage: the percentage of memory used by the five algorithms varies by the number of users in each scenario. Scenario II: Faulty Scenario  
We conducted new experiments using a failure scenario, where one of the six edge servers loses 25% of the number of CPUs available (dropping from eight to six CPUs) and 50% of its RAM (dropping from 16 MBytes to 8 MBytes). The other five edge servers maintain their maximum capacity in the simulation, 8 CPUs and 16 MBytes, respectively. We chose this configuration because it represents a common failure situation that can be challenging to troubleshoot. In this scenario, all the nodes remain online, but one is under-equipped. * Mean user delay: In Figure 15, we compared the variation in mean user delay between Ubunye and the other algorithms as we varied the number of users. With the failure scenario, we saw that the DPDA algorithm had the best performance, with Ubunye BW falling to second place, followed by first-fit, Ubunye latency, and round robin. However, as we increased the number of users, we noticed a degradation in the DPDA performance and an improvement in the performance of the Ubunye BW and latency algorithms, which regained first and second place, respectively, in the scenario with 150 users. * Total user delay: Figure 16 shows that the Ubunye BW and latency algorithms recovered better than others as we increased the number of users. * Mean bandwidth: Figure 17 shows the results of measuring the mean bandwidth available on the established network links during the first ten minutes of the simulation in the 150-user scenario. Even with the failure scenario, the Ubunye latency and BW algorithms obtained the best results regarding the bandwidth offered to users. * CPU resources: We compared the percentage of total CPU consumption of the edge nodes between the five algorithms, varying the number of users in each scenario. Despite the failure to increase the overall utilization of all the algorithms, the Ubunye BW and latency algorithms had lower CPU consumption due to their load-balancing characteristics, as shown in Figure 18.
* Memory resources: We compared the percentage of edge node memory utilization between the five algorithms, varying the number of users in each scenario. Figure 19 shows that despite the failure to increase the overall utilization of all the algorithms, the Ubunye BW and latency algorithms obtained lower CPU consumption due to their load-balancing characteristics. Ubunye latency obtained the best overall performance. * User requests success rate: The Ubunye algorithms maintained a success rate for user requests similar to other algorithms by incorporating bandwidth as a QoE criterion in edge resource allocation. The Ubunye latency algorithm outperformed the Ubunye BW algorithm, securing second place among the five analyzed algorithms, as shown in Figure 20.  
Figure 15: Mean user delay (in milliseconds): the ratio between the sum of the delays of all user requests and the total number of requests, varying the number of users in each faulty scenario. Figure 11:0 Total user delay (in seconds): the total sum of user delay over the total simulation time, varying the number of users in each faulty scenario. Figure 11:1 Mean bandwidth available on the faulty scenario (in megabits per second): measured in the first ten minutes of the simulation for the 150-user scenario. Figure 11:2 Consumption of CPU resources: percentage of the total CPU consumption for the five algorithms, varying the number of users in each faulty scenario.