# An Uncertainty-Driven Proactive Self-Healing Model for
## 3 Results
### Performance Assessment  
Figure 3 presents the performance of the models in terms of the metrics \(\phi\), \(\eta\), \(\omega\), and \(\psi\), while \(\theta=85\%\) and \(\beta=0.80\). In these results, we notice that the BM and the DM execute a higher percentage of tasks than the UPSHM. However, when we focus on the percentage of high-priority tasks executed locally, we can easily come to the conclusion that the proposed model clearly outperforms the BM and the DM by executing all high-priority tasks locally. Additionally, as far as the percentage of tasks with high demand is concerned, the UPSHM exhibits better performance, executing almost all of these tasks locally. In the last metric, i.e., \(\psi\), the ability of our model to maintain free space in the local memory for high-priority tasks is indicated, since the queue of the node did not become full during the \(W\) time instances. Moreover, the DM achieves equal performance in the same metric. In summary, from the results of the first experiment we can clearly observe the significant dominance of our model in the performance metrics that concern the execution of the high priority and high demand tasks. The time comparison between the models for \(\theta=85\%\) and \(\beta=0.8\) is presented in Figure 4. As we can easily observe, the UPSHM requires more time than BM and DM to make an offloading decision. Figure 3: Comparison of results for all performance metrics, for \(\theta=85\%\) and \(\beta=0.8\). In the second experimental scenario, we set \(\beta=0.88\) and keep \(\theta=85\%\). Figure 5 depicts the performance of BM, UPSHM and DM for metrics \(\phi\), \(\eta\), \(\omega\) and \(\psi\). We notice that the BM executes slightly lower percentages of received tasks than the UPSHM and DM. Nevertheless, when we examine the percentage of high-priority tasks executed locally, we can easily perceive that the difference in the performance among the BM, DM and UPSHM is noticeably higher. In the same figure, we can also see the behavior of the models in the execution of tasks with high demand and also the frequency of the local queue having no space for all types of tasks. We can easily come to the conclusion that the UPSHM has the best performance for the \(\omega\) metric by executing more tasks with high demand in contrast to the BM and DM. Additionally, concerning the \(\psi\) metric, we notice that both the UPSHM and the BM exhaust the capacity of the local queue several times in contrast to DM. This means that in this specific experimental scenario, using either BM or UPSHM for the management of the incoming tasks, the node is forced to offload the incoming tasks without considering their priority or their demand. Even in this case, though, the UPSHM outperforms the BM with regard to the \(\psi\) metric, as the number of times that the local queue is full is significantly less than the BM. Nevertheless, the DM achieves the best performance in this metric. Figure 4: Time comparison between the models for \(\theta=85\%\) and \(\beta=0.8\). Figure 5: Comparison of results for all performance metrics, for \(\theta=85\%\) and \(\beta=0.88\). In Figure 6 we see the comparison between the models based on the \(AvT\); similar to the previous scenario, the UPSHM needs more time to make the offloading decision in contrast to the other models. From the previous two experimental scenarios, we observe that the UPSHM charges the node with more computational load than the BM and DM. However, we can accept this extra computational load because the node achieves clearly better performance in the metrics \(\phi,\eta,\omega\) and \(\psi\). The performance of the individual node we are studying is not affected by the rest of the nodes in the network since the communication of the node with other nodes is not required to apply UPSHM. In Figure 7, we present a comparison of the performance of the UPSHM for different values of \(\beta\) and \(\theta=85\%\). We can observe by the values of the \(\phi\) metric that the higher the \(\beta\) is, the more tasks are executed locally.