## 6 Experimental Evaluation
### Experiment II: Service Allocation
#### 6.2.5 Service Allocation Results  
We assessed Ubunye's computational efficiency by analyzing the CPU and memory resource usage percentage. To evaluate the user experience, we measured average user delay, total user delay, average available bandwidth, and the success rate in responding to user requests. Scenario I: Default Scenario  
We consider all edge nodes to work without fault in the following experiments. * Mean user delay: This is 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. We compared the variation in mean user delay between Ubunye and the other existing algorithms as we varied the number of users. Figure 9 shows that the Ubunye BW and Ubunye latency algorithms achieved the lowest average user delay values among the tested options for user counts ranging from 30 to 150. This success can be attributed to their focus on prioritizing bandwidth (Ubunye BW) and network latency (Ubunye latency), as well as effectively balancing CPU and memory resources. The first-fit and round robin algorithms performed similarly to each other. In contrast, the DPDA algorithm, which has a more complex decision-making process for allocating services to edge servers, resulted in the worst performance. Our proposal obtained a gain of up to 1.8% compared to the classic algorithms (first-fit and round robin) and 9.9% compared to the DPDA algorithm. The Student's \(t\)-test confirmed the samples as statistically significant in all scenarios, with a value of around \(4.85\times 10^{-11}\). * Total user delay: We compared the variation in total user delay, representing the total sum of user delay over the total simulation time, between our proposal and the other algorithms as we vary the number of users. Figure 10 shows our algorithm with the lowest total user delay for the same reasons given in the previous paragraph for all the algorithms. Our proposal obtained a gain of up to 2.1% compared to the classic algorithms (first-fit and round robin) and 9.9% compared to the DPDA algorithm. The Student's \(t\)-test confirmed the samples as statistically significant in all scenarios. * Mean bandwidth: Figure 11 shows the mean bandwidth available, measured in the first ten minutes of the simulation for the 150-user scenario. Our algorithm outperformed others, showing a 22% improvement over the first-fit and round robin algorithms and 47% over the DPDA algorithm. The Student's \(t\)-test confirmed the samples as statistically significant in all scenarios. Additionally, the Ubunye latency algorithm surpassed the Ubunye BW algorithm by 11.4% during the observed interval. * CPU resources: We analyze the percentage of total CPU consumption for five algorithms while varying the number of users in each scenario. The results in Figure 12 show that Ubunye had lower CPU consumption due to its efficient load-balancing feature. * Memory resources: When measuring the memory usage of edge servers (Figure 13), Ubunye consistently achieved the lowest percentage of memory consumption. This positive outcome significantly enhanced the overall performance of the proposed topology in the simulation across all scenarios. * User request success rate: The Ubunye maintained a success rate for user requests that was similar to that of other algorithms. This resulted from incorporating bandwidth as a QoE criterion when allocating edge resources in the proposed topology, as illustrated in Figure 14. The Ubunye latency algorithm improved this indicator compared to the Ubunye BW algorithm, securing the second position in the comparison of the five analyzed algorithms. Figure 11: Mean bandwidth available (in megabits per second): measured in the first ten minutes of the simulation for the 150-user scenario. Figure 10: Total user delay (in seconds): the total sum of user delay over the total simulation time, varying the number of users in each scenario.