## 5 Analysis of Results  
The obtained results demonstrate that FHS presents advantages over Kubernetes' HPA, especially regarding customization and reduction of the required number of replicas. Figure 16: Distribution of values associated with the average rate of packets received in rpm per replicas during 180 s for all \(\beta\) values tested. Figure 17: Distribution of values associated with the average number of replicas during 180 s for all \(\beta\) values tested. Figure 18: Distribution of the average associated with the violation rate for a latency SLA of \(\mathrm{SLA}_{\mathrm{max}}=250\,\mathrm{ms}\). During the 90 experiments we carried out with different stress scenarios (different \(\beta\) values) generated by JMeter, it was observed that FHS managed to maintain a lower CPU consumption compared to HPA. Figures 9 and 12 illustrate these results during the 180 s of two specific tests (\(\beta=9\) and \(\beta=49\)), and Figure 15 reinforces this result for all tests conducted. Figure 9 shows that the CPU consumption of HPA is higher because it significantly increases the number of replicas to 120 s, resulting in a peak CPU load. This peak occurs because the workload profile is at its maximum between approximately 40 seconds and 120 seconds. With more replicas (see Figure 11), HPA exhibits greater CPU consumption. On the other hand, Figure 9 reveals that the packet reception rate per replica is reduced in HPA due to the higher number of replicas. In contrast, FHS, which uses fewer replicas, results in lower CPU consumption and a higher packet reception rate per replica, as shown in Figures 9-11. In Figure 12, it can be observed that the CPU consumption of HPA is significantly higher due to the rapid increase in replicas up to 120 seconds, resulting in peak CPU load. This behavior results from the workload profile, which is at its maximum between approximately 40 s and 120 s. With a higher number of replicas, HPA shows increased CPU consumption. In contrast, Figure 13 shows that the packet reception rate per replica is lower in HPA due to the large number of replicas (see Figure 14). On the other hand, FHS uses fewer replicas, resulting in lower CPU consumption and a higher packet reception rate per replica. Figure 12 highlights that while the workload decreases after 120 s, the number of replicas in HPA does not decrease at the same rate, maintaining higher CPU consumption for a more extended period. This behavior underscores the efficiency of FHS in dynamically adjusting the number of replicas to provide more balanced and efficient resource utilization over time. Although the workload decreases after 120 s, the number of replicas in HPA does not decrease at the same rate, allowing higher CPU consumption to be maintained for a more extended period. In contrast, FHS dynamically adjusts the number of replicas more efficiently, resulting in more balanced and efficient resource consumption over time. This difference in behavior between HPA and FHS highlights the ability of FHS to manage resources more effectively, ensuring better CPU utilization and higher efficiency in packet delivery per replica and validating its application in real-world edge computing scenarios. The fact that FHS uses a smaller number of replicas compared to HPA in all experiments contributes to the reduction in CPU rate. With fewer replicas in operation, the workload is distributed more efficiently among them, resulting in lower CPU consumption per replica. This indicates better utilization of available resources and a reduction in system overhead. This feature can be advantageous in stress scenarios, as it allows for more precise optimization and a better balance between resource demand and system capacity. Figures 11 and 14 illustrate these results during the 180 s of two specific tests (\(\beta=9\) and \(\beta=49\)). Meanwhile, Figure 17 details this result for all the tests conducted. As shown in Figures 10, 13, and 16, FHS results in a higher packets rate in rpm per replica compared to HPA.